Pharmacogenomic Decision Support for Modulators of the NMDA, Glycine, and AMPA Receptors

ABSTRACT

Methods for identifying patients diagnosed with treatment resistant or refractory depression, pain or other clinical indications who are eligible to receive N-methyl-D-aspartate receptor antagonist, glycine receptor beta (GLRB) modulator, or α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-based therapies to include determining the appropriate medication, an optimal dose for each patient, and determining which patients are not eligible to receive the therapy. The pharmacogenomic clinical decision support assays include targeted single nucleotide polymorphisms and clinical values or a combination of targeted single nucleotide polymorphisms, targeted ketamine-specific expansion and contraction of topologically associated domains, and clinical values. The methods described herein allow for a more effective determination of which patients will experience drug efficacy and which patients will experience adverse drug events. The methods provide personalized patient recommendations for dose, the frequency of medication administration, and recommendations on drug choice.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of the filing dateof (1) provisional U.S. Application Ser. No. 62/795,705, filed on Jan.23, 2019, entitled “Methods and Systems to Reconstruct Drug SpatialNetworks from Pharmacogenomic Regulatory Interactions and Uses Thereof,”and (2) provisional U.S. Application Ser. No. 62/795,710, filed on Jan.23, 2019, entitled “Companion Diagnostic Assays for N-methyl-D-AspartateReceptor Modulators,” the entire disclosures of each of which is herebyexpressly incorporated by reference herein.

FIELD OF THE INVENTION

The techniques described herein pertain to pharmacogenomic clinicaldecision support assays useful for the selection of N-methyl-D-aspartate(NMDA) receptor, glycine receptor, andα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)receptor-based therapies for depression (especially treatment-resistantor refractory depression) and other clinical indications includinganesthesia and analgesia/pain disorders, neuropsychiatric disorders, andneurological disorders using ketamine and its enantiomers as examples.More specifically, the techniques relate to specific biomarkers,including genetic markers, clinical values, and disease phenotypesderived from a patient to optimize selection of medications that impactthese receptor networks and doses for an individual patient.

BACKGROUND

Existing antidepressant medications are not effective for many patients.A new class of antidepressant drugs are being developed that targetglutamate receptors in human forebrain. Ketamine(RS-2-chlorophenyl-2-methylamino-cyclohexanone), a glutamateN-methyl-d-aspartate receptor (NMDAR) noncompetitive antagonist,approved by the U.S. Food & Drug Administration (FDA) as an anesthetic,has shown promise as an antidepressant in patients withtreatment-resistant depression (TRD). Although the racemic formula mayhave potent and undesirable psychotomimetic and other side effectsdepending upon several variables, chemical analogs of ketamine exhibitdiminished adverse events. Intravenous and oral formulations havedemonstrated efficacy and tolerability in controlled trials andopen-label studies across patient populations known to often achievelittle to no response from traditional antidepressants that target theserotonin transporter (SLC6A4, also called 5HTT or SERT1), includingserotonin-norepinephrine reuptake inhibitors (SNRIs). Evidence suggeststhat ketamine, its enantiomers, and ketamine analogs exert theirmechanism of action primarily through modulation of the NMDA receptor(NMDAR) and downstream receptors in this network in the human brain.

The pharmacodynamic (PD) target for ketamine-like drugs is an NMDAR thatconsists of GRIN1 and GRIN2 subunits, which binds glutamate andN-methyl-D-aspartate, a binding site for glycine and D-serine encoded byGLRB, as well as sites that bind polyamines, histamine and cations.Antagonists, partial antagonists and receptor modulators such asketamine and its and other NMDAR and glycine modulators, includingphencyclidine, amantadine, dextromethorphan, tiletamine, riluzole,methoxetamine, methoxphenidine and memantine, block inward Ca⁺² influx,preventing postsynaptic depolarization. Neuroimaging studies havedemonstrated that intravenous infusion of ketamine causes a transientsurge in glutamate levels observed in prefrontal cortex in concert witha rapid antidepressant effect. It has been shown that following NMDARblockade, glutamate preferentially binds to the GRIA1, GRIA2 and GRIA4subunits of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid(AMPA) receptor. Several NMDAR antagonists and partial antagonists, GLRBmodulators and AMPAR agonists are in development for therapy ofrefractory depression, but exhibit dissociative effects in patients. Inaddition, NMDAR antagonists, GLRB antagonists and AMPAR modulators,partial antagonists and receptor modulators that act in the same networkas this class of drugs that have failed in clinical trials based onsafety concerns might be repurposed using the methods of this disclosureto improve their chances of success in clinical trials.

The three dimensional (3D) architecture of the human regulatoryepigenome plays a major role in determining human phenotype. It is nowacknowledged that the majority of significant single nucleotidepolymorphisms (SNPs) associated with disease risk, drug response, andother human traits found in genome-wide association studies (GWAS) arelocated within enhancers, promoters and other non-coding regulatoryelements. Coupled with recent insights into the organization of thefunctional 4D Nucleome, an abundance of biomedical “big data” has becomeaccessible in open source and proprietary resources, enabling thereconstruction of drug regulatory pathways in the human genome. Newmethods are being applied for the mining of regulatory variants fromgenomewide association studies (GWAS), phenome-wide association studies(PheWAS), and results from mining electronic health record data andclinical trial data. It is now standard practice in the art that singlenucleotide polymorphism (SNP) trait-associations from these data sourcesare re-evaluated in the context of pathway analysis, and have been foundto be significant because they resolve to the same or related biologicalnetworks. Combining an abundance of biomedical data coupled withinnovative methods for mining biological networks has provided afoundation for the detection of gene variants that may impactvariability in drug response, including adverse drug events. Thisapproach promises tremendous advances in specialties such as psychiatry,in which a lack of drug efficacy and an abundance of adverse drug eventshave proven especially problematic in patient care.

Over half of all Americans will exhibit the symptoms of a psychiatricdisorder during their lifetime. The most prevalent lifetime psychiatricdisorders are stress and anxiety disorders, mood disorders, includingmajor depressive disorder and bipolar disorder, impulse-controldisorders, substance use disorders and schizoaffective disorders. Thelifetime prevalence of any psychiatric disorder in the U.S. is 53%,while 28% have two or more lifetime disorders and 18% have three ormore, showing that comorbidity of psychiatric disorders is a significantmedical challenge. Although some psychiatric disorders, such as bipolar1 disorder are inherited in families at approximately an 80% penetrationrate, others exhibit no obvious heritability. For major depressivedisorder (MDD), genetic factors play important roles in the etiology ofthe disease, as indicated by family, twin, and adoption studies. Twinstudies suggest a heritability of 50%, and family studies indicate atwofold to threefold increase in lifetime risk of developing MDD amongfirst-degree relatives. Several socio-demographic variables aresignificantly related to lifetime risk of psychiatric disorders instudies that controlled for cohort. For example, females (biologicalsex) have a significantly higher risk than men of anxiety and majordepressive disorder, and males (biological sex) have a significantlyhigher risk than females of impulse-control and substance abusedisorders. Non-Hispanic blacks and Hispanics have a significantly lowerrisk than non-Hispanic whites of anxiety, mood, and substance abusedisorders, and low education is associated with a high risk of substanceabuse disorders. The data shows that many factors, ranging fromenvironmental and sociological factors, biological sex, ethnicity andfamilial genetics, all contribute to the etiology of psychiatricdisease. In addition, the complexity of phenotype within an individualpatient or cohort of patients, including comorbidities with otherpsychiatric disorders and stress-related diseases, necessitates a rangeof distinct algorithmic classification solutions, including machinelearning, as well as multiple statistical analyses, including linearregression, to accurately specify precision therapy beyond what iscurrently available.

Psychiatric illness has a greater impact on human health than any otherdisease. For example, major depressive disorder (MDD) causes a greaterburden of disability worldwide than any other medical conditionincluding cancer, heart disease, stroke, chronic obstructive pulmonarydisease, and HIV/AIDS, yet it remains the most undiagnosed, misdiagnosedand untreated or poorly treated disease known to humankind. In 2013, theU.S. National Institutes of Health (NIH) provided 13 times more fundingfor research in oncology than for depression—˜$5.3 billion versus $415million. In the U.S. from 2009-2011, adverse events related toprescribed antidepressants amounted to over 25,000 visits to theemergency room on an annual basis, resulting in 30% of all prescriptiondrug-related hospitalizations each year. Patients with major depressivedisorder and comorbid medical conditions experience more severe symptomsof depression and lower rates of response and remission withantidepressant treatment compared with patients with no comorbidconditions. Treatment-resistant depression (TRD) constitutes 30-40% ofall patients diagnosed with MDD and is defined as “failure to achieveremission after two well-established antidepressant courses known tohave been of evidence-based acceptable dose and duration.”

Contemporary antidepressant medications are not effective in manypatients, and in patients that do respond or remit, weeks to months ofpharmacotherapy are required before the alleviation of symptoms isachieved. Consequently, newer and more effective antidepressantmedications are being developed. For example, both the racemic mixtureof ketamine and the S-enantiomer of ketamine are examples ofN-methyl-D-aspartate receptor (NMDAR) partial antagonists that have beenapproved by the U.S. Food and Drug Administration (FDA) for treatment ofTRD. Ketamine elicits a rapid antidepressant response and concomitantelevated levels of glutamate in cortex for approximately 50% of TRDpatients as measured by the Montgomery-Åsberg Depression Rating Scale(MADRS) total score. Although R, S-ketamine has been used for clinicalindications such as chronic pain, peri-operative analgesia and sedationsince 1970, adverse drug events (AEs) are common following ketaminetreatment, and diversion is limited by restricting use to inpatient andoutpatient treatment settings. For example, in the phase III clinicaltrial of Esketamine for TRD prior to submission to the FDA, almost aquarter of the TRD patients experienced severe dissociative effects, 2deaths were reported, and an additional 6.9% of TRD patients in thetreatment arm experienced severe psychotomimetic effects includingdelirium, delusion and suicidal ideation, as well as suicide attempts.

One of the challenges in psychiatry is precise matching ofpharmacotherapy to accurately address the complex symptomatology of theindividual patient. Psychiatric patients exhibit extensive comorbiddisorders, and there are few objective biomarkers that can be used asdiagnostic criteria to accurately tailor antidepressant, antipsychoticand anti-manic therapy to the patient. Although diagnostic rating scalessuch as the Hamilton Scale for Depression (HAM-D) exhibit goodinter-rater reliability, psychiatric disorders such as depressionpresent as various distinct phenotypes. Non-pharmacological therapiesmay exhibit improved efficacy in patients with TRD or recurrentdepression. For example, repetitive transcranial magnetic stimulation(rTMS) shows promise as a non-medication therapeutic alternative forpatients suffering from TRD; however, the best outcomes in TRD occurwhen rTMS is used as an adjunct to traditional antidepressantpharmacotherapy, as is the case for the antidepressant class ofmedications that include an NMDAR antagonist or partial antagonist, GLRBmodulator, or AMPAR agonist, which can only be provided in a clinicalsetting to a patient who is already taking another antidepressantmedication.

rTMS therapy requires dozens of clinical visits, remission is highlyvariable among patients with TRD, and rTMS is effective in only about20-40% of cases, in which remission from depression lasts for as long as1-2 years. Recent results from rTMS combined with neuroimagingdemonstrate specific clustering of depressed patients into 4 distinctphenotypes along axes of anhedonia and anxiety based on differentialrTMS array placement. These results provide substantive evidence that itis possible to stratify psychiatric patients by phenotype based onactivation of different brain connectivity pathways, networks whichexhibit considerable inter-individual variability among patients, andgreatly improves the opportunity for precise matching of optimal therapyto the individual patient.

Although rTMS offers promise for patients with TRD, its mechanism ofaction remained elusive until independent research studies demonstratedthat TMS first acts in the subgenual anterior cingulate cortex andsignificantly increases glutamate levels along with biomarkers ofN-methyl-d-aspartate receptor (NMDAR) modulation.

These are remarkable findings, as they demonstrate that the mechanism ofrTMS brain activation is virtually indistinguishable from that ofketamine's pharmacotherapeutic mechanism of action. Thus, rTMS andketamine exhibit similar mechanisms of action to relieve TRD, althoughabout half of all TRD patients do not remit after treatment using eithertherapeutic option. In addition, both rTMS and ketamine exhibittransient but serious AEs, including dissociation (the presumptive basisof ketamine's analgesic efficacy), psychotomimesis, and neurocognitiveimpairment. This suggests that it is critically important to matchindividual patients to one of these therapies or to other antidepressantmedications if we can choose which patient will benefit from thesetreatments and which patient will suffer unnecessarily from serious AEswithout adequate antidepressant efficacy.

Recent research combining transcranial magnetic stimulation (TMS) toalleviate depression followed by neuroimaging demonstrated that TRDpatients can be unambiguously stratified into 4 subtypes based on theirresponse to placement of the TMS device, with 4 different intrinsicneuroanatomical pathways activated concomitant with distinctly differentsymptom clusters. These 4 subtypes can now be determined independently,as demonstrated by this disclosure, using a combination of clinical andmolecular data, thereby providing an exemplar for other psychiatricdisorders and stress-related disorders, in which improvedpharmacophenomic decision support will deliver better therapeuticoptions to the patient. Similarly, NMDAR antagonist therapy may be usedas an adjunct to age-related degenerative medical conditions.

SUMMARY

Different methods may be used to accurately determine the precisetherapeutic requirements for an individual patient phenotype or cohortof phenotypes. This disclosure describes methods for the configurationof a pharmacophenomic assay for clinical decision support, or acompanion diagnostic for a psychotropic medication, which optimizes thefit of a therapeutic intervention to an individual patient or cohort ofpatients diagnosed with a psychiatric or related disorder, such astreatment-resistant depression, chronic pain, migraine, fibromyalgia,inflammatory disorders and other conditions in which ketamine or one itsanalogs comprise an effective therapeutic. In the context of thisdisclosure, the patient's drug response and adverse event phenotype iscomprised of multiple sets of variables as described herein, rangingfrom a patient's intrinsic configuration of a drug's pharmacogenomicnetwork including its mutational profile configuration, to behavioralphenotypes that may be obtained from clinical data.

The methods used for patient stratification in this disclosure usedisparate data sources, some of which may be incomplete, require datacleansing and/or curation, or may be non-existent. The different methodsas described herein range from those that may accommodate differentcombinations of limited data to more extensive computational solutions,or which may bridge missing data elements using probabilistic methods.

This disclosure comprises a range of concatenated and distinct methodsto provide accurate pharmacophenomic decision support for a patientdiagnosed with a psychiatric disorder. Outputs provide quantitativescores for ranking therapeutic interventions including recommendationssuch as medication selection and dose, transcranial magneticstimulation, electroconvulsive therapy and behavioral intervention. Thisdisclosure comprises pharmacophenomic methods to classify patientsdiagnosed with a psychiatric disorder into subtypes for optimization oftherapeutic intervention. These methods can be used to configure adiagnostic to recommend the best therapeutic match to an individualpatient. In another embodiment, these methods may be used to enhance theselection of patients based on pharmacophenomic stratification prior toa clinical trial. In another embodiment, these methods may be used toconfigure a companion diagnostic for a psychotropic medication to ensurepatient safety during the development, marketing and post-marketing of apharmaceutical.

In another embodiment, clinical values are obtained from an EHR orsimilar source, and SNPs in PD and PK genes are obtained from thegenotype of a patient, and these are entered as quantitative values in aregression equation (nomogram) for determination of medication dose fora drug such as ketamine for that individual patient. In this embodiment,the therapeutic dose optimum is developed in a step-wise regressionmodel equation containing genetic and clinical values, and theregression model is re-tested and validated using a population ofpatients to ensure the accuracy of the regression equation's output, asmight be judged by a receiver-operator (ROC) curve as area under thecurve (AUC).

In another embodiment, clinical values are obtained from an EHR orsimilar source, and SNPs in PD and PK genes are combined with diseaserisk SNPs obtained from genomewide association studies (GWAS) todifferentially annotate adverse event and efficacy-specific sub-networksof a drug pharmacogenomic network, such as that of ketamine, to predictwhether the patient would benefit from the drug or not, and if so, bedeterminative of a patient-appropriate dosage.

In another embodiment, clinical values are obtained from an EHR orsimilar source, and SNPs in PD and PK genes are combined with diseaserisk SNPs obtained from GWAS and PheWAS to differentially annotateadverse event and efficacy-specific sub-networks of a drugpharmacogenomic network, such as that of ketamine, to predict whetherthe patient would benefit from the drug or not, and if so, bedeterminative of a patient-appropriate dosage. In this embodiment,therapeutic drug monitoring through pharmacometabolomics is used togather more accurate data on pre-existing prescribed and non-prescribeddrugs and their metabolites used by the patient, through analysis of abiological sample (blood, cheek swab, urine or other bodily fluid)obtained from a patient or from a cohort of patients.

In yet another embodiment, clinical values are obtained from an EHR orsimilar source, and SNPs in PD and PK genes are combined with diseaserisk SNPs obtained from GWAS and PheWAS to differentially annotateadverse event and efficacy-specific sub-networks of a drugpharmacogenomic network, such as that of ketamine, and these data arematched with 1 of 4 phenotypes, which may or may not be derived fromrTMS and neuroimaging data), determined using scoring from the HamiltonDepression Rating Scale (HAMD) in the context of an antidepressant drugsuch as ketamine.

In another embodiment, pharmacophenomic decision support is determinedusing inputs from a model that includes: (1) molecular profiling ofdrug-induced sub-networks in a patient or cohort of patients, (2)clinical variables as derived from an electronic health record orequivalent measurements made by a clinician, (3) patient subtyping basedon clinical variables and neuroimaging studies, and (4) PD and PK SNPsthat stratify patients by drug response. In addition, drug-drug anddrug-gene interactions objectively measured using a pharmacometabolomicmethod can be used to minimize adverse drug events for the individualpatient or a cohort of patients.

Another embodiment of this system is configuration of a companiondiagnostic that can be used for patient selection for a clinical trial,and during the marketing and post-marketing phases of a drug, such as anantidepressant medication that acts as a NMDAR antagonist, partialantagonist, GLRB modulator and AMPAR agonist.

Another embodiment of this system is to determine and select atherapeutic medication addition to a NMDAR modulator to improve outcome.Another embodiment of the methods and system described herein could beused for the re-evaluation of drugs for clinical trials and for drugre-purposing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a block diagram of a computer network and system onwhich an exemplary companion diagnostic system may operate in accordancewith the presently described embodiments;

FIG. 1B is a block diagram of an exemplary drug and dose decision serverthat can operate in the system of FIG. 1A in accordance with thepresently described embodiments;

FIG. 1C is a block diagram of an exemplary client device that canoperate in the system of FIG. 1A in accordance with the presentlydescribed embodiments;

FIG. 1D illustrates a flow diagram representing an exemplary method fordetermining a drug and dosage to administer to a patient suffering fromdepression or other neuropsychiatric illness based on comparing datafrom the patient's biological sample to a reference drug-specificpharmacogenomic network and the constituent sub-networks for the drug ofinterest;

FIG. 2 illustrates example measurements collected from a patient'sbiological sample using chromosome conformation capture, bioinformaticsanalysis, and/or similar measures;

FIGS. 3A-3B show a simple example of how a SNP located within anenhancer in the network might disrupt the enhancer's contact with one ofits target gene promoters in the TAD, leading to adverse drug events ina patient within a drug response cohort. FIG. 3A illustrates howdifferent laboratory methods may be used to obtain measures from thechromatin spatial interactome in three dimensions and analyze the dataas 2 dimensional plots of enhancer-gene promoter interactions. FIG. 3Bdepicts how a SNP may disrupt a chromatin loop between an enhancer andone of two gene promoters that it regulates within a TAD. Thisdisruption removes the spatial connection between the enhancer and genepromoter 1 resulting in dysregulation of gene 1, resulting in an adverseevent in this patient and its cohort in response to administration ofthe particular drug of interest;

FIGS. 4-1 and 4-2 illustrate a flow diagram and scoring systemrepresenting an exemplary method for determining a the proper drug anddosage while avoiding adverse events (AEs) to administer to a patientsuffering from depression or other neuropsychiatric illness based oncomparing data from the patient's biological sample to a referencedrug-specific pharmacogenomic network and the constituent sub-networksfor the drug of interest;

FIG. 5 illustrates a flow diagram representing an exemplary method forusing efficacy and adverse event sub-networks derived from an individualpatient's pharmacogenomic network to prescribe a given drug at a givendose safely, proceed with caution, or to stop recommendation toprescribe using molecular phenotype quantitative assays generated fromthe drug's sub-networks;

FIG. 6 illustrates 2 different methods based on the post hocbioinformatics analysis and annotation of disease risk SNPs from GWAS todetermine that the patient of a specific ancestry should receiveketamine based on disease gene risk variant analysis and the presence ofdisease risk SNPs associated with ketamine's adverse event sub-network 3and efficacy sub-network 2;

FIG. 7 illustrates how fine-tuning of a range of sub-network typesspanning the range of human drug response phenotypes is accomplishedusing machine learning against a model created from a highlyheterogenous set of hierarchal biomedical and biological data types andelements as a compared with that of the patient input sample data aftera particular drug is chosen from a database of reference set of drugpharmacogenomic networks and their constituent sub-networks;

FIG. 8 illustrates a flow diagram of an exemplary method for usingsimilarity scores to match a patient's drug pharmacodynamic efficacy andadverse events to that of a reference drug pharmacogenomic network fornovel pharmacodynamic target discovery using TAD matrix mapping and deeplearning based on computer vision algorithms to allow for TAD patternsto be used to investigate drug similarity and/or use TAD matchingmethods in clinical trial investigating drug TAD profiles;

FIG. 9A illustrates an exemplary model for how the system integratesheterogeneous hierarchal biomedical and biological data and processesthese multi-scale data using machine learning and deep learning forpharmacogenomic network topology and sub-network reconstruction. Thisstrategy for mapping drug networks provides insight into the mechanisticon- and off-target effects. Using the discovered pharmacogenomic networktopologies provides the basis for advanced pharmacogenomics decisionsupport, laying a foundation for subsequent preclinical and clinicalstudies to refine this capability, which can also be used for drugmechanism discovery and drug repurposing prediction;

FIG. 9B illustrates a flow diagram representing a method for generatinga reconstructed drug pharmacogenomic network and correspondingsub-networks for a drug of interest, including the human pharmacogenomicSNP input filter, the drug spatial network reconstruction engine, andthe iterative gene set optimization engine. Reference sets of these drugpharmacogenomic networks, drug efficacy, and drug adverse eventinformation can also be created;

FIG. 10 illustrates a flow diagram representing an exemplary method forintegrative drug-gene set optimization to construct a pharmacogenomicnetwork and to determine into its sub-networks;

FIG. 11 illustrates a flow diagram for post hoc validation of areconstructed drug pharmacogenomic network and sets of such networksusing bioinformatics data/software and integrative pharmacoinformaticspipelines;

FIG. 12 illustrates an example of a comparison of the results ofsignificance testing of the SNP rs12967143-G, an intragenic enhancerlocated in the TCF4 gene, versus other GWAS SNPs as described using thenumerical output from six different machine learning algorithms used inthe analysis and among various neural and non-neural cell types;

FIG. 13 illustrates characteristics of the 2 different ketaminepharmacogenomic sub-networks as determined from a post hoc validation ofthe ketamine pharmacogenomic network in the human brain. FIG. 13A isgene enrichment for the ketamine pharmacogenomic sub-network in thehuman brain that mediates efficacy and neuroplasticity. FIG. 13B is geneenrichment for the ketamine pharmacogenomic sub-network in the humanbrain that mediates glutamate receptor signaling and adverse events inthe human brain;

FIG. 14A illustrates graphical depictions of the ketaminepharmacogenomic sub-network in the human brain that mediates efficacyand neuroplasticity, and the diseases and conditions that are associatedwith efficacy and neuroplasticity;

FIG. 14B illustrates graphical depictions of the ketaminepharmacogenomic sub-network in the human brain that mediates glutamatereceptor signaling and adverse events in the human brain, and thediseases and conditions that are associated with this signaling andthese adverse events;

FIG. 15 lists the genes and regulatory RNAs located in the ketamineefficacy and neuroplasticity sub-network;

FIG. 16 lists the genes located in the ketamine glutamate receptorsignaling and adverse event sub-network;

FIG. 17 lists the genes located in the ketamine pharmacokinetics andhormonal regulation sub-network;

FIGS. 18A-18B illustrate example values of a linear regression analysisfor ketamine dose determination and the accuracy of the ketamine dosingin a validation cohort;

FIG. 19 illustrates definitions of four treatment-resistant depression(TRD) patient subtypes as determined by transcranial magneticstimulation (TMS) coupled with neuroimaging of resting connectivitynetworks in human brain;

FIG. 20 illustrates example neuromaps for the four different subtypes ofTRD depressed patients;

FIG. 21 illustrates example drug prescribe/do not prescriberecommendations and alternative medication options for each of the fourdifferent subtypes of TRD depressed patients;

FIG. 22 illustrates an example of beneficial combinatorial mechanismsand therapeutics mediated by synergistic histone modificationsdiscovered using the methods described herein using valproic acid andketamine in H3K9 acetylation and deacetylation respectively, leading toneurogenesis and neuro-differentiation, in combination;

FIG. 23 illustrates the complementary pharmacogenomic network ofvalproic acid, FIG. 23A, and the pharmacogenomic network of ketamine,FIG. 23B, showing neurogenesis and neuro-differentiation, respectively;and

FIG. 24 illustrates the combinatorial and biologically synergisticactions of valproic acid and ketamine pharmacogenomic networks inneurogenesis, neuronal proliferation and terminal neuronaldifferentiation.

DETAILED DESCRIPTION

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this disclosure. The detailed description is to beconstrued as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical, if not impossible. Numerous alternative embodiments couldbe implemented, using either current technology or technology developedafter the filing date of this patent, which would still fall within thescope of the claims.

It should also be understood that, unless a term is expressly defined inthis patent using the sentence “As used herein, the term ‘ ’ is herebydefined to mean . . . ” or a similar sentence, there is no intent tolimit the meaning of that term, either expressly or by implication,beyond its plain or ordinary meaning, and such term should not beinterpreted to be limited in scope based on any statement made in anysection of this patent (other than the language of the claims). To theextent that any term recited in the claims at the end of this patent isreferred to in this patent in a manner consistent with a single meaning,that is done for sake of clarity only so as to not confuse the reader,and it is not intended that such claim term be limited, by implicationor otherwise, to that single meaning. Finally, unless a claim element isdefined by reciting the word “means” and a function without the recitalof any structure, it is not intended that the scope of any claim elementbe interpreted based on the application of 35 U.S.C. § 112, sixthparagraph.

This disclosure comprises a system and methods for stratification ofpatients or cohort of patients diagnosed with a psychiatric disorder orrequiring these drugs for other clinical indications for accuratemedication selection and dose of a NMDAR antagonist. Ketamine is used asan exemplar, but these methods can be used for NMDAR antagonists orα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptormodulators that are in clinical trials for a clinical indication ofrefractory depression. Ketamine and its enantiomers are novelantidepressants that exhibit greater efficacy and reduced side effectscompared to other antidepressants for a patient or specific subset ofpatients diagnosed with TRD. The pharmacogenomic decision support systemcan determine which patients diagnosed with TRD should receive ketamineor one its enantiomers as an antidepressant medication, and if so, whatthe appropriate dosage should be for an individual patient or cohort ofpatients to maximize efficacy, minimize adverse drug events anddrug-drug interactions, and reduce the harmful effects of drug-gene anddrug-drug interactions. The adverse psychotropic side effects, includingpsychotomimetic and neurocognitive effects of glutamate receptortargeted medications for alleviation of depression, coupled with theheterogeneity of the patient population who may exhibittreatment-resistant depression (TRD) is based on multiple variables,including demographic and sociological variables, trauma history,genotype and clinical variables.

The system includes several methods that can be used for configurationof a clinical decision support diagnostic for selection and dosing ofketamine or one its enantiomers as an antidepressant in refractorydepression and may be generalized to other psychotropic medications. Oneembodiment includes an integrated multi-scale measurement system thatcomprises the components shown in FIG. 1D, where patient biosamples canbe extensively analyzed as shown in FIG. 2 and other relevant clinicaldata may also be obtained.

In some embodiments, the system is configured to analyze the minimalamount of results from the biosample required to match a stored cohortsub-network reference set. In this case, patient data is compared with areference set of drug sub-networks that span the entire range of humandrug response cohorts using a learning machine which has beenpre-trained on the cohort response range for a particular drug, and thematch to a reference set prompts a clinical decision recommendation. Forexample, the reference set of drug sub-networks may include a set ofreference drug pharmacodynamic efficacy sub-networks, reference drugpharmacodynamic adverse event sub-networks, reference chromatinremodeling sub-networks, and reference pharmacokinetic enzymes andhormones sub-networks for the particular drug.

An electronic encryption broker is first used to protect healthinformation through de-identification and to prevent patientidentification. Biological sample(s) (e.g., blood, cheek swab, saliva,urine or other bodily fluid) are obtained from a patient or cohort ofpatients with accompanying clinical data from a medical record, such asan electronic health record (EHR) or other source. Initialpharmacometabolomic analysis on small blood samples, or plasmacomponents thereof, collected from a patient or cohort of patients isperformed for determination of potential drug-drug and drug-geneinteractions that might alter subsequent pharmacogenomic decisionsupport. These objective measurements augment self-reported,clinician-reported or other data contained in an EHR or another patientrecord.

Generally speaking, techniques for determining whether to administer adrug to a patient, such as a glutamate NMDAR antagonist or partialantagonist, GLRB modulator, or AMPAR agonist, and/or determining theappropriate dosage of the drug to administer to the patient may beimplemented in one or several client devices, one or several networkservers, or a system that includes a combination of these devices.However, for clarity, the examples below focus primarily on anembodiment in which a health care professional obtains a patient'sbiological sample and provides the biological sample to an assaylaboratory for analysis.

The biological sample may include the subject's skin, blood, urine,sweat, lymph fluid, bone marrow, cheek cells, saliva, cell lines,tissues, etc. Cells are then extracted from the biological sample andreprogrammed into stem cells, such as induced pluripotent stem cells(iPSCs). Then the iPSCs are differentiated into various tissues, such asneurons, cardiomyocytes, etc., and assayed to obtain genomic data,chromosomal data, metabolomic data, etc. for the patient. In someembodiments, the iPSCs may be assayed for loci associated with orcausatively associated a phenotypic response to the drug of interest.iPSCs comprise part of the reference set used to derive variables forassessing individual patients.

The drug and dose decision server relies on the drug and dose decisionsupport engine. This engine receives a numerical score representing theoverlap between an input patient sample as shown in FIG. 2 with therelevant drug-specific reference pharmacogenomic network stored in adatabase of such references 154. The system uses a learning machinetrained on the entire range of human drug response cohorts, whichconsists of the pharmacogenomic network reference set for a particulardrug, encompassing the range of human drug response variation to aparticular drug that comprises the reference set to match to the inputpatient sample.

The drug and dose decision server analyzes the laboratory results todetermine a sub-network representation for the patient for a drug geneset for an NMDAR antagonist or partial antagonist, GLRB modulator, orAMPAR agonist such as ketamine. Additionally, the drug and dose decisionserver retrieves a reference pharmacogenomic network and constituentreference sub-networks for the NMDAR antagonist or partial antagonist,GLRB modulator, or AMPAR agonist for example, from a reference drugpharmacogenomic network database. Then the drug and dose decision servercompares the drug sub-network representation for the patient to thereference pharmacogenomic network and constituent reference sub-networksfor the drug to determine whether to administer the drug to the patient.For example, the drug and dose decision server may compare an efficacydrug-specific (e.g., ketamine) sub-network for the patient to areference efficacy drug-specific (e.g., ketamine) sub-network and maycompare an adverse event drug-specific (e.g., ketamine) sub-network forthe patient to a reference adverse event drug-specific (e.g., ketamine)sub-network. The drug and dose decision server may then determine thatthe patient should be administered the drug if the similarity betweenthe efficacy drug-specific sub-network for the patient and the referenceefficacy drug-specific sub-network is greater than a threshold(indicating the drug is likely to be effective on the patient). The drugand dose decision server may also determine that the patient should beadministered the drug if the similarity between the adverse eventdrug-specific sub-network for the patient and reference adverse eventdrug-specific sub-network is below a threshold (indicating the patientis unlikely to experience adverse events), or based on some combinationof the two.

Accordingly, the drug and dose decision server may provide arecommendation to a health care professional's client device indicatingthat the patient should receive the drug, thereby causing the healthcare professional to administer the drug to the patient. As a result,the health care professional may administer the drug to the patient. Insome embodiments, the drug and dose decision server may determine adosage of the drug to administer to the patient according to a dosingalgorithm. The dosing algorithm may be determined using machine learningtechniques such as linear regression and may be based on demographicdata for the patient, clinical data for the patient, biological data forthe patient, etc.

The drug and dose decision server may determine the dosage of the drugto administer to the patient and perform other methods described hereinusing various machine learning techniques, including, but not limited toregression algorithms (e.g., ordinary least squares regression, linearregression, logistic regression, stepwise regression, multivariateadaptive regression splines, locally estimated scatterplot smoothing,etc.), instance-based algorithms (e.g., k-nearest neighbors, learningvector quantization, self-organizing map, locally weighted learning,etc.), regularization algorithms (e.g., Ridge regression, least absoluteshrinkage and selection operator, elastic net, least-angle regression,etc.), decision tree algorithms (e.g., classification and regressiontree, iterative dichotomizer 3, C4.5, C5, chi-squared automaticinteraction detection, decision stump, M5, conditional decision trees,etc.), clustering algorithms (e.g., k-means, k-medians, expectationmaximization, hierarchical clustering, spectral clustering, mean-shift,density-based pharmacogenomic clustering of applications with noise,ordering points to identify the clustering structure, etc.), associationrule learning algorithms (e.g., a priori algorithm, Eclat algorithm,etc.), Bayesian algorithms (e.g., naïve Bayes, Gaussian naïve Bayes,multinomial naïve Bayes, averaged one-dependence estimators, Bayesianbelief network, Bayesian network, etc.), artificial neural networks(e.g., perceptron, Hopfield network, radial basis function network,etc.), deep learning algorithms (e.g., multilayer perceptron, deepBoltzmann machine, deep belief network, convolutional neural network,stacked autoencoder, generative adversarial network, etc.),dimensionality reduction algorithms (e.g., principal component analysis,principal component regression, partial least squares regression, Sammonmapping, multidimensional scaling, projection pursuit, lineardiscriminant analysis, mixture discriminant analysis, quadraticdiscriminant analysis, flexible discriminant analysis, factor analysis,independent component analysis, non-negative matrix factorization,t-distributed stochastic neighbor embedding, etc.), ensemble algorithms(e.g., boosting, bootstrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machines, gradient boosted regressiontrees, random decision forests, etc.), reinforcement learning (e.g.,temporal difference learning, Q-learning, learning automata,State-Action-Reward-State-Action, etc.), support vector machines,mixture models, evolutionary algorithms, probabilistic graphical models,etc.

Referring to FIG. 1A, an example pharmacogenomic decision support system100 determines whether to administer a psychotropic drug to a patientsuffering from depression such as a patient with TRD, and theappropriate dosage for the psychotropic drug. The pharmacogenomicdecision support system 100 includes a drug and dose decision server 102and a plurality of client devices 106-116 which may be communicativelyconnected through a network 130, as described below. In an embodiment,the drug and dose decision server 102 and the client devices 106-116 maycommunicate via wireless signals 120 over a communication network 130,which can be any suitable local or wide area network(s) including a WiFinetwork, a Bluetooth network, a cellular network such as 3G, 4G,Long-Term Evolution (LTE), 5G, the Internet, etc. In some instances, theclient devices 106-116 may communicate with the communication network130 via an intervening wireless or wired device 118, which may be awireless router, a wireless repeater, a base transceiver station of amobile telephony provider, etc. The client devices 106-116 may include,by way of example, a tablet computer 106, a smart watch 107, anetwork-enabled cell phone 108, a wearable computing device such asGoogle Glass™ or a Fitbit® 109, a personal digital assistant (PDA) 110,a mobile device smart-phone 112 also referred to herein as a “mobiledevice,” a laptop computer 114, a desktop computer 116, wearablebiosensors, a portable media player (not shown), a phablet, any deviceconfigured for wired or wireless RF (Radio Frequency) communication,etc. Moreover, any other suitable client device that record clinicaldata for patients may also communicate with the drug and dose decisionserver 102.

Each of the client devices 106-116 may interact with the drug and dosedecision server 102 to receive a recommendation on whether to administerthe psychotropic drug to the patient and the dosage for the psychotropicdrug. The client device 106-116 may present the recommendation via auser interface for display to a health care professional.

In an example implementation, the drug and dose decision server 102 maybe a cloud based server, an application server, a web server, etc., andincludes a memory 150, one or more processors (CPU) 142 such as amicroprocessor coupled to the memory 150, a network interface unit 144,and an I/O module 148 which may be a keyboard or a touchscreen, forexample.

The drug and dose decision server 102 may also be communicativelyconnected to a database 154 of reference drug pharmacogenomic networksand constituent sub-networks such as efficacy and adverse-eventsub-networks for the drug.

The memory 150 may be tangible, non-transitory memory and may includeany types of suitable memory modules, including random access memory(RAM), read only memory (ROM), flash memory, other types of persistentmemory, etc. The memory 150 may store, for example instructionsexecutable of the processors 142 for an operating system (OS) 152 whichmay be any type of suitable operating system such as modern smartphoneoperating systems, for example. The memory 150 may also store, forexample instructions executable on the processors 142 for a drug anddose decision support engine 146. The drug and dose decision server 102is described in more detail below with reference to FIG. 1B. In someembodiments, the drug and dose decision support engine 146 may be a partof one or more of the client devices 106-116, the drug and dose decisionserver 102, or a combination of the drug and dose decision server 102and the client devices 106-116.

In any event, the drug and dose decision support engine 146 may obtainlaboratory results from the patient biosample only as is necessary tomatch one of the set of pharmacogenomic networks and their constituentsub-networks that define human drug response variation for theparticular drug. These include molecular data that includes variation inthe genome defined by SNPs in PD and PK genes, pharmacogenomicinteractions between regulatory elements, genes in a patient's genomethat can be defined using chromosome conformation data such as Hi-C,and/or direct topologically associating domain (TAD)-specific measures,including differential gene expression determined using RNA sequencing(RNA-Seq) or expression microarray profiling and patient-specific TADcontactome measures in relevant or surrogate cell types using chromosomeconformation capture (e.g., 3C, 4C, 5C, Hi-C, ChIA-PET and GAM). Themolecular data may be assayed for loci associated with or causativelyassociated a phenotypic response to the drug of interest. Additionally,the drug and dose decision support engine 146 may obtain a referencepharmacogenomic network and constituent reference sub-networks for thepsychotropic drug of interest (e.g., ketamine) from a reference drugpharmacogenomic network database 154.

Then the drug and dose decision support engine 146 may analyze thelaboratory results for the patient to determine a sub-networkrepresentation for the psychotropic drug of interest, such as anefficacy sub-network and an adverse event sub-network. The drug and dosedecision support engine 146 may compare the efficacy sub-network and anadverse event sub-network for the patient to reference efficacy andadverse event sub-networks to determine whether to administer thepsychotropic drug of interest to the patient. If the similarity betweenthe efficacy drug-specific sub-network for the patient and the referenceefficacy drug-specific sub-network is greater than a threshold and/orthe similarity between the adverse event drug-specific sub-network forthe patient and reference adverse event drug-specific sub-network isbelow a threshold, the drug and dose decision support engine 146 maydetermine that the patient should be administered the psychotropic drugof interest. The drug and dose decision support engine 146 may thenprovide a recommendation to a health care professional's client device106-116 indicating that the patient should receive the psychotropic drugof interest. Otherwise, the drug and dose decision support engine 146may provide a recommendation of another drug to administer to thepatient to treat depression. Furthermore, the drug and dose decisionsupport engine 146 may determine a dosage of the psychotropic drug ofinterest to administer to the patient according to a dosing algorithm.The drug and dose decision support engine 146 may also provide arecommended dosage for the psychotropic drug of interest to the healthcare professional's client device 106-116.

The drug and dose decision server 102 may communicate with the clientdevices 106-116 via the network 130. The digital network 130 may be aproprietary network, a secure public Internet, a virtual private networkand/or some other type of network, such as dedicated access lines, plainordinary telephone lines, satellite links, combinations of these, etc.Where the digital network 130 comprises the Internet, data communicationmay take place over the digital network 130 via an Internetcommunication protocol.

Turning now to FIG. 1B, the drug and dose decision server 102 mayinclude a controller 224. The controller 224 may include a programmemory 226, a microcontroller or a microprocessor (MP) 228, arandom-access memory (RAM) 230, and/or an input/output (I/O) circuit234, all of which may be interconnected via an address/data bus 232. Insome embodiments, the controller 224 may also include, or otherwise becommunicatively connected to, a database 239 or other data storagemechanism (e.g., one or more hard disk drives, optical storage drives,solid state storage devices, etc.). The database 239 may include datasuch as drug pharmacogenomic network reference data, drug recommendationdisplay templates, web page templates and/or web pages, and other datanecessary to interact with users through the network 130. The database239 may include similar data as the database 154 described above withreference to FIG. 1A.

It should be appreciated that although FIG. 1B depicts only onemicroprocessor 228, the controller 224 may include multiplemicroprocessors 228. Similarly, the memory of the controller 224 mayinclude multiple RAMs 230 and/or multiple program memories 226. AlthoughFIG. 1B depicts the I/O circuit 234 as a single block, the I/O circuit234 may include a number of different types of I/O circuits. Thecontroller 224 may implement, for example, the RAM(s) 230 and/or theprogram memories 226 as semiconductor memories, magnetically readablememories, and/or optically readable memories.

As shown in FIG. 1B, the program memory 226 and/or the RAM 230 may storevarious applications for execution by the microprocessor 228. Forexample, a user-interface application 236 may provide a user interfaceto the drug and dose decision server 102, which user interface may, forexample, allow a system administrator to configure, troubleshoot, ortest various aspects of the server's operation. A server application 238may operate to receive molecular data for a patient, analyze themolecular data to determine sub-networks for the patient related to aparticular drug of interest, compare the sub-networks for the patient toreference sub-networks for the particular drug of interest, determine toadminister the particular drug of interest to the patient based on thecomparison, and transmit a recommendation to administer the particulardrug of interest to the patient to a client device 106-116. The serverapplication 238 may be a single module 238 such as the drug and dosedecision support engine 146 or a plurality of modules 238A, 238B.

While the server application 238 is depicted in FIG. 1B as including twomodules, 238A and 238B, the server application 238 may include anynumber of modules accomplishing tasks related to implementation of thedrug and dose decision server 102. Moreover, it will be appreciated thatalthough only one drug and dose decision server 102 is depicted in FIG.1B, multiple drug and dose decision servers 102 may be provided for thepurpose of distributing server load, serving different web pages, etc.These multiple drug and dose decision servers 102 may include a webserver, an entity-specific server (e.g. an Apple® server, etc.), aserver that is disposed in a retail or proprietary network, etc.

Referring now to FIG. 1C, the laptop computer 114 (or any of the clientdevices 106-116) may include a display 240, a communication unit 258, auser-input device (not shown), and, like the drug and dose decisionserver 102, a controller 242. Similar to the controller 224, thecontroller 242 may include a program memory 246, a microcontroller or amicroprocessor (MP) 248, a random-access memory (RAM) 250, and/or aninput/output (I/O) circuit 254, all of which may be interconnected viaan address/data bus 252. The program memory 246 may include an operatingsystem 260, a data storage 262, a plurality of software applications264, and/or a plurality of software routines 268. The operating system260, for example, may include Microsoft Windows®, OS X®, Linux®, Unix®,etc. The data storage 262 may include data such as application data forthe plurality of applications 264, routine data for the plurality ofroutines 268, and/or other data necessary to interact with the drug anddose decision server 102 through the digital network 130. In someembodiments, the controller 242 may also include, or otherwise becommunicatively connected to, other data storage mechanisms (e.g., oneor more hard disk drives, optical storage drives, solid state storagedevices, etc.) that reside within the laptop computer 114.

The communication unit 258 may communicate with the drug and dosedecision server 102 via any suitable wireless communication protocolnetwork, such as a wireless telephony network (e.g., GSM, CDMA, LTE,etc.), a Wi-Fi network (802.11 standards), a WiMAX network, a Bluetoothnetwork, etc. The user-input device (not shown) may include a “soft”keyboard that is displayed on the display 240 of the laptop computer114, an external hardware keyboard communicating via a wired or awireless connection (e.g., a Bluetooth keyboard), an external mouse, amicrophone for receiving voice input or any other suitable user-inputdevice. As discussed with reference to the controller 224, it should beappreciated that although FIG. 1C depicts only one microprocessor 248,the controller 242 may include multiple microprocessors 248. Similarly,the memory of the controller 242 may include multiple RAMs 250 and/ormultiple program memories 246. Although the FIG. 1C depicts the I/Ocircuit 254 as a single block, the I/O circuit 254 may include a numberof different types of I/O circuits. The controller 242 may implement theRAM(s) 250 and/or the program memories 246 as semiconductor memories,magnetically readable memories, and/or optically readable memories, forexample.

The one or more processors 248 may be adapted and configured to executeany one or more of the plurality of software applications 264 and/or anyone or more of the plurality of software routines 268 residing in theprogram memory 246, in addition to other software applications. One ofthe plurality of applications 264 may be a client application 266 thatmay be implemented as a series of machine-readable instructions forperforming the various tasks associated with receiving information at,displaying information on, and/or transmitting information from thelaptop computer 114.

One of the plurality of applications 264 may be a native applicationand/or web browser 270, such as Apple's Safari®, Google Chrome™,Microsoft Internet Explorer®, and Mozilla Firefox® that may beimplemented as a series of machine-readable instructions for receiving,interpreting, and/or displaying web page information from the drug anddose decision server 102 while also receiving inputs from a user such asa health care professional or researcher. Another application of theplurality of applications may include an embedded web browser 276 thatmay be implemented as a series of machine-readable instructions forreceiving, interpreting, and/or displaying web page information from thedrug and dose decision server 102.

One of the plurality of routines may include a drug recommendationdisplay routine 272 which presents a recommendation of whether toadminister a psychotropic drug of interest to a patient and/or arecommended dosage on the display 240.

Preferably, a user may launch the client application 266 from a clientdevice, such as one of the client devices 106-116 to communicate withthe drug and dose decision server 102 to implement the companiondiagnostic system 100. Additionally, the user may also launch orinstantiate any other suitable user interface application (e.g., thenative application or web browser 270, or any other one of the pluralityof software applications 264) to access the drug and dose decisionserver 102 to realize the companion diagnostic system 100.

FIG. 1D illustrates a flow diagram representing an exemplary method 160for determining a drug and dosage to administer to a patient sufferingfrom depression based on comparing data from a patient's biologicalsample to a reference pharmacogenomic network and constituentsub-networks for the drug of interest. The method 160 may be executed bythe drug and dose decision server 102.

In some embodiments, patient bio-samples are analyzed as shown in FIG.1D for personalized therapy to quantify the relative activation of thedifferent pathways that mediate ketamine's mechanisms of action in thehuman CNS determined using the methods described herein. Patient orpatient cohort biosamples may be analyzed using pharmacometabolomicassays for determination of drug or metabolites in samples that maycause unwanted drug-drug-interactions, impacting the efficacy, adverseevents and dosing of the medication. Biosample measurement includes: (1)genotyping of pharmacokinetic SNPs, in the exemplar of ketamineconsisting of mutations in the CYP2B6 gene that have been shown to bedeterminative of metabolizer status (poor, subnormal, normal orultra-rapid subtypes), (2) pharmacodynamic SNP targeting as inputs intopharmacogenomic network and sub-network profiling, which isdeterministic of both efficacy and adverse events as analyzed using thepharmacogenomic genome classifier and pharmacodynamic sub-networkprofiling systems, (3) direct topologically associating domain(TAD)-specific measures including differential gene expressiondetermined using RNA sequencing (RNA-Seq) or expression microarrayprofiling, and patient-specific TAD contactome measures in relevant orsurrogate cell types using chromosome conformation capture (e.g., 3C,4C, 5C, Hi-C, ChIA-PET and GAM), and (4) pharmacometabolomic analysis.

The data processing pipeline shown in FIG. 1D comprises parallel routesfor analysis of the biosample using multiple methods, and analysis ofavailable clinical data for the same patient as might be collected froman electronic health record (EHR). This example illustrates theanalytics used to determine whether a specific patient should beadministered an N-methyl-D-aspartate receptor (NMDAR) modulator. Theadverse events associated with NMDAR modulators such as ketamine may besevere, including severe dissociation, hallucinations and nightmares.Thus, the first decision to be made from these parallelized analytics isto ensure that the patient does not receive NMDAR modulators such asketamine if the system predicts that the individual will experiencemoderate to serious adverse events.

The data processing pipeline shown in FIG. 1D may determine a “no-go”decision on administration of NMDAR modulators such as ketamine based onthe molecular network representation in the patient, certain diseaserisk SNPs that the patient may harbor as shown in FIG. 23, thetreatment-resistant depression phenotype of the patient as shown in FIG.20 and FIG. 22, as well as a dose adjustment based on thepharmacometabolic profile of the patient. If the system decides that thepatient may receive an NMDAR modulator such as ketamine, a dosedetermination algorithm is then initiated.

The method 160 is utilized as a clinical decision support diagnostic todetermine whether the patient should be prescribed an NMDAR modular asan antidepressant (block 162) and the optimal dosage for the patient. Insome embodiments, patient bio-samples are analyzed (block 164 a) forpersonalized therapy to quantify the relative activation of thedifferent pathways that mediate ketamine's mechanisms of action in thehuman CNS determined using the methods described herein. Patient orpatient cohort biosamples may be analyzed using pharmacometabolic assaysfor determination of drug or metabolites in samples that may causeunwanted drug-drug-interactions, impacting the efficacy, adverse eventsand dosing of the medication. At block 166, biosample measurementsinclude: (1) genotyping of pharmacokinetic SNPs, in the exemplar ofketamine consisting of mutations in the CYP2B6 gene that have been shownto be determinative of metabolizer status (poor, subnormal, normal orultra-rapid subtypes), (2) pharmacodynamic SNP targeting as inputs intopharmacogenomic network and sub-network profiling, which isdeterministic of both efficacy and adverse events as analyzed using thepharmacogenomic genome classifier and pharmacodynamic sub-networkprofiling systems, (3) direct topologically associating domain(TAD)-specific measures including differential gene expressiondetermined using RNA sequencing (RNA-Seq) or expression microarrayprofiling and patient-specific TAD contactome measures in relevant orsurrogate cell types using chromosome conformation capture (e.g., 3C,4C, 5C, Hi-C, ChIA-PET and GAM), and (4) pharmacometabolomic analysis(block 164 c).

As described above, biosample measurements include pharmacodynamic SNPtargeting as inputs into pharmacogenomic network and sub-networkprofiling (blocks 168, 170 b), which is deterministic of both efficacyand adverse events as analyzed using the pharmacogenomic genomeclassifier and pharmacodynamic sub-network profiling systems. At block170 a, a reference pharmacogenomic network and sub-networks for the drugof interest are retrieved from a reference database. The patient'ssub-networks for the particular drug of interest, which include efficacyand adverse event sub-networks, are then compared to the referencepharmacogenomic network and sub-networks for the drug of interest (block172). For determination of similarity to the reference set, the twodifferent pairs of reference-patient metrics include an accuratemeasurement of similarity and outputs similarity scores for each of theefficacy and adverse events sub-networks. At block 172, the similarityscores for the efficacy and adverse event sub-networks for the drug ofinterest may be used to determine whether to administer the drug ofinterest to the patient. For example, if the similarity score for theefficacy sub-network is above a threshold similarity score, the method160 may determine that the drug of interest should be administered tothe patient (block 176). Otherwise, the method 160 determines to selecta different medication (block 174).

In addition to comparing the patient's sub-networks for the drug ofinterest to reference sub-networks for the drug of interest, at block164 b clinical data is collected and analyzed for the patient todetermine whether to administer the drug to the patient and/or thedosage for the drug. More specifically, the patient's HAMD score and/orpatient symptoms may be analyzed to categorize the patient into one offour TRD patient subtypes (block 180). The TRD patient subtypes aredescribed in more detail below with reference to FIGS. 15-17. If thepatient is categorized as TRD subtype 3 (block 182), the method 160determines to select a different medication (block 184). Other clinicaldata may also be analyzed such as the patient's drug-drug interactions,age, weight, biological sex, body mass index, ethnicity, family history,patient history of substance abuse, diagnostic codes, hospitalizationhistory, drug-gene interactions, mental illness history, whether thepatient smokes or uses nicotine, etc. (block 186).

Then a dosage of the drug of interest is determined to administer to thepatient (block 178). The dosage may be determined based on a dosingalgorithm having predetermined constants to apply to each of severalpatient characteristics, such as biological characteristics, demographiccharacteristics, clinical characteristics, etc. In other embodiments,the dosing algorithm may be generated using machine learning techniques.The patient characteristics utilized in the dosing algorithm may includebiological data, such as SNPs that have been reported to stratifyresponse to ketamine in humans. The patient characteristics may alsoinclude demographic data for the patient, such as the patient's sex,height and weight, age, and ethnicity. Furthermore, the patientcharacteristics may include clinical data, such as family history,drug-drug interactions, mental illness history, whether the patientsmokes or uses nicotine, and Hamilton Scale for Depression (HAM-D)score.

FIG. 2 shows various measures that may be undertaken from the patient'sbiosample. In one embodiment, blood and buccal swab samples are obtainedand processed. For ketamine and other medications that undergo firstpass metabolism by the protein encoded by the hyper-inducible andhyper-variable CYP2B6 gene, targeted SNP genotyping is performed using a4-SNP panel, but most importantly, examination of the splicing variantSNP rs3745274 is prioritized, because it is relatively common (>10%frequency) among human populations, and carriers of this SNP compriseultra-poor metabolizers of any drug which is primarily metabolized bythis enzyme, and it is very likely to experience adverse drug eventsfrom an NMDAR modulator such as ketamine.

In the method illustrated in FIG. 1D and in the measurements collectedFIG. 2, a pharmacometabolomic analysis of the patient's blood isrecommended to rule out any possibility of negative drug-drug ordrug-gene interactions.

FIG. 2 illustrates the analytics that may be performed on the patient'sbiosample to obtain the minimal information necessary to permit amachine or deep learning algorithm to match the drug-specific pattern ofthe reference set of comprehensive sub-networks that comprise activationof the topologically associating domains (TADs) contained in a database154. These methods may include measurements of TAD-specific changes ingene expression using RNA-seq or expression microarrays, pharmacogenomiccontacts between genes using chromosome conformation capture analysissuch as Hi-C, and/or targeted genotyping following induction ofgeometric alterations in the pharmacogenomic genome by the drug, eitherafter administration of the particular drug to the patient, orproactively, through analysis of a buccal swab obtained from the patientprior to deciding whether the drug should be administered or not.

The biosample measurements in FIG. 2 include genotyping of targetedpharmacokinetic and pharmacodynamic SNPs (block 292), directTAD-specific measures including differential gene expression andpharmacogenomic contacts (block 294), and pharmacometabolomics (block296). Genotyping of targeted pharmacokinetic and pharmacodynamic SNPsincludes identifying mutations in the CYP2B6 gene that have been shownto be determinative of metabolizer status (block 282), and classifyingthe strength of efficacy and adverse events sub-networks according topharmacogenomic network and sub-network profiling (block 284). Thedirect chromatin contact-specific measures include differential geneexpression determined using circumscribed RNA sequencing (RNA-Seq)(block 286), and circumscribed chromosome conformation capture analysisto identify pharmacogenomic contacts (block 288). Moreover, thepharmacometabolomic analysis is used to identify pre-existing drugs andmetabolites in the patient's biosample to assess potential drug-druginteractions, for example (block 290), and can be used to periodicallymonitor patient drug-drug interactions and drug adherence.

FIG. 3 shows a simple example of how a SNP located within an enhancer orsuperenhancer in the network might disrupt the enhancer's contact withone of its target gene promoters in the TAD, leading to adverse drugevents in a patient within a drug response cohort. FIG. 3A illustrateshow different laboratory methods may be used to obtain measures from thechromatin pharmacogenomic interactome in three dimensions and analyzethe data as 2 dimensional plots of enhancer-gene promoter interactions.FIG. 3B depicts how a SNP may disrupt a chromatin loop between anenhancer and one of two gene promoters that it regulates within a TAD.This disruption removes the pharmacogenomic connection between theenhancer and gene promoter 1 resulting in dysregulation of gene 1,resulting in an adverse event in this patient and its cohort in responseto administration of the particular drug of interest.

FIG. 4 illustrates the first of several embodiments for matchingreference data to a patient input for clinical decision support. In thisexample, the pharmacogenomic drug sub-network reference set for aparticular drug may be combined with a biosample input of sparse resultsfrom a patient and using a combinatorial co-training method be used toderive a drug efficacy score for decision-making.

For example, as shown in FIG. 5, the drug and dose decision server 102compares the patient's efficacy and adverse event sub-networks for apsychotropic drug of interest to reference efficacy and adverse eventsub-networks for the psychotropic drug of interest. In this example,reference sub-network 1 (ref. no. 502) and reference sub-network 2 (ref.no. 504) are adverse event sub-networks, while reference sub-network 3(ref. no. 506) is an efficacy sub-network. Patient X's sub-networks 510are dissimilar from the adverse event sub-networks (referencesub-networks 1 and 2 (ref. nos. 502, 504)) and similar to the efficacysub-network (reference sub-network 3 (ref. no. 506)). Accordingly, thedrug and dose decision server 102 determines that the psychotropic drugof interest should be administered to the patient 520. Patient Y'ssub-networks 512 are dissimilar from one of the adverse eventsub-networks (reference sub-network 1 (ref. no. 502)) and similar to theother adverse event sub-network and the efficacy sub-network (referencesub-networks 2 and 3 (ref. nos. 504, 506)). Due to the adverse events insub-network 2 (ref. no. 504), the drug and dose decision server 102determines that the psychotropic drug of interest should be administeredto the patient but with a reduced dosage 522. Patient Z's sub-networks514 are similar to both adverse event sub-networks (referencesub-networks 1 and 2 (ref. nos. 502, 504)) and dissimilar from theefficacy sub-network (reference sub-network 3 (ref. no. 506)), and thus,the drug and dose decision server 102 determines not to administer thepsychotropic drug of interest to the patient 524.

FIG. 5 provides examples of how bioinformatics analytics can be used inmatching a drug's reference set of antidepressant efficacy versusadverse event signatures from the gene set optimizer with those of aninput patient biosample. In FIG. 5, patient X can be administeredketamine based on the match of antidepressant efficacy from sub-network2 of the reference pharmacogenomic network and reduced match tosub-network 3 of adverse events to antidepressant efficacy from thepharmacogenomic network that will be experienced by the individual.However, in patient Z, since this individual harbors some of the diseaserisk SNPs from GWAS found in sub-networks of the pharmacogenomicnetwork, patient Z's ketamine dose should be adjusted.

FIG. 6 illustrates how two different methods may be derived from andbased on the post hoc bioinformatics analysis or annotation of diseaserisk SNPs from GWAS to determine that the patient should not receiveketamine based on disease gene risk variant analysis and the presence ofdisease risk SNPs associated with ketamine's adverse event sub-networkand efficacy sub-network of the pharmacogenomic drug network forketamine. These simple and preliminary methods may be used to initiallyscreen a patient as to potential negative consequences of ketamineadministration.

Biosamples 2002, 2004 are collected from patient A and patient B.Patient A's biosample 2002 is analyzed to perform pharmacodynamic SNPstargeting as inputs into pharmacogenomic network and sub-networkprofiling to determine efficacy and adverse event sub-networks forPatient A (block 2006). Patient B's biosample 2004 is analyzed toidentify pharmacokinetic SNPs associated with ketamine response (block2008). Patient A's efficacy and adverse event sub-networks are thencompared to a reference pharmacogenomic network and reference efficacyand adverse event sub-networks for ketamine (block 2010). Patient B'sSNPs are compared to SNPs included in reference pharmacogenomic networkand reference efficacy and adverse event sub-networks for ketamine(block 2012). In FIG. 6, patient A dose must be adjusted prior toadministration of ketamine based on the matching of ketamine sub-networkmediating adverse events to that of antidepressant efficacy from thepharmacogenomic network that will be experienced by the individual andthe reduced match of antidepressant efficacy sub-network of thereference pharmacogenomic network (block 2014). Also, in patient B,since this individual harbors many of the disease risk SNPs from GWASfound in sub-networks of the pharmacogenomic network, the dose ofketamine administered to patient B should be adjusted (block 2016).

FIG. 7 illustrates a comprehensive strategy in which a library spanninghuman drug response cohort phenotypes stored in a reference database areused to make an initial “go or no-go” decision about whether ketamineshould be administered or not for a specific patient, and how otherclinical data from a particular patient may be fine-tuned to make aninformed clinical decision.

FIG. 8 illustrates a flow diagram that uses well known pattern matchingalgorithms from deep learning in computer vision to use similarityscores to match a patient's drug efficacy and adverse events to that ofa reference drug pharmacogenomic network. FIG. 8 also illustrates howthis strategy may be used for the discovery of novel, similar drugs.

FIG. 9A illustrates an overview of the integrative, multiscale dataanalysis used in the system. FIG. 9B illustrates a flow diagramrepresenting an exemplary method for generating a reconstructed drugpharmacogenomic network and corresponding sub-networks for a drug ofinterest, including the human pharmacogenomic SNP input filter, the drugpharmacogenomic network reconstruction engine, and the iterative geneset optimization engine.

As described above, biosample measurement includes pharmacodynamic SNPtargeting as inputs into pharmacogenomic network and sub-networkprofiling, which is deterministic of both efficacy and adverse events asanalyzed using the pharmacogenomic genome classifier and pharmacodynamicsub-network profiling systems. The patient's sub-networks for aparticular drug of interest are then compared to a referencepharmacogenomic network and its constituent sub-networks for the drug ofinterest. FIG. 9B illustrates a method 300 for identifying a referencepharmacogenomic network and constituent sub-networks for a particulardrug of interest, such as ketamine. In some embodiments, the drug anddose decision server 102 executes the method 300 to identify thepharmacogenomic network and constituent sub-networks for a particulardrug of interest and stores the pharmacogenomic network and constituentsub-networks in the reference pharmacogenomic network database 154. Inother embodiments, the method 300 is executed by another computingdevice and the output of the method is provided to the drug and dosedecision server 102 and stored in the reference pharmacogenomic networkdatabase 154.

Selection of SNPs

In any event, at block 302, SNPs are obtained from human clinicalstudies that have demonstrated significant association with response andadverse events to the drug of interest. Since the location of a SNPassociated with the trait under study has been, in most cases,inaccurately assigned to the nearest gene or nearby candidate gene inthe published literature and GWAS per the linear sequence of thereference human genome assembly, accurate localization using imputationand annotation techniques are used to determine the actual location ofthe reported SNP.

New research has several important implications for drug pharmacogenomicnetwork identification. First, new drug target mechanisms can beidentified by collecting pharmacogenomic network outputs in a trainingset through the use of computer vision-based TAD matching using deeplearning (machine learning) and validation using correspondence to knowndrug-induced genomewide TAD matrices. Second, the clustering of new drugtarget mechanisms in previously defined but incompletely informedbiological pathways will increase the probability of success. Third,insight gained using three dimensional (3D) genome architecture todetermine drug targets from pharmacogenomic GWAS will lead to a nextgeneration of drug candidates and greatly enhance the accuracy ofpharmacogenomic clinical decision support diagnostics.

At block 304, pharmacodynamics, pharmacokinetic, and other SNPs areevaluated using a pharmacogenomics informatics pipeline. Thepharmacogenomics informatics pipeline uses lead SNPs reported from GWASand candidate gene studies to find genetically linked permissivecandidate SNPs using TAD boundary instead of measures of linkagedisequilibrium. These SNPs are evaluated with two separate workflows:the enhancer regulatory workflow for regulatory SNPs and the coding SNPworkflow. The enhancer regulatory SNP workflow evaluates the permissivecandidate SNPs in disease-relevant tissues for DNA methylation,transcription factor binding, histone marks, DNase I hypersensitivity,chromatin state, quantitative trait loci (QTLs) and transcription factorbinding site disruption using tissue-specific omics datasets. The codingSNP workflow finds common nonsynonymous coding SNPs within the pool ofpermissive candidate SNPs, which are then examined for histonemodifications ruling out exon-containing enhancer SNPs. Both sets ofSNPs are mapped back to their TADs and host genes and screened forexpression in relevant tissues. The final output SNPs are then evaluatedusing open source machine learning algorithms to determine if the SNP iscausal or not (block 306), and the causal variants are kept for furtheranalysis in the workflow (block 308). Exon SNPs are also evaluated assplice donors or splice acceptors using the Altrans algorithm. If theyare found to be involved in alternative splicing, they are stored assuch.

Use of Casual Enhancer SNPs for Interrogation

At block 310, enhancer SNPs are used as probes to determine target geneswithin the same TAD as the enhancer is located, and to determinepharmacogenomic trans-interactions with other TADs using Hi-C chromosomeconformation capture and ChIA-PET datasets (block 314) generated fromcell types and tissues in which the drug of interest acts. Genes, whichherein includes other functional elements such as long non-coding RNAs,are located within the same TADs that are targets of the enhancer thatsignificantly alters drug response in human populations are selected forthe drug pharmacogenomic network, if the TADs have strong boundaries aspredicted by the amount of bound CTCF and significant association withsuper-enhancers (block 312). In the TADs that comprise the top 3statistically significant pharmacogenomic contacts of the first set ofpharmacogenomic TADs within the same cell and/or tissue type in whichthe drug of interest acts are then evaluated, and genes within these“trans-TADs” are chosen if they are controlled by the same cell and/ortissue-specific enhancers in which the drug of interest acts (block316).

At block 318, the combined set of genes are evaluated forinter-connectivity, where the combined set of genes are selected fromthe first set of TADs that harbor the pharmacogenomic SNPs and the genesselected from the “trans-TADs”, comprising the genes controlled inconcert with the first set of TAD genes. For example, third-partysoftware may be utilized, such as Ingenuity Pathway Analysis™, forexamination of connectivity of the combined set of genes. Using Fisher'sright-sided exact test, if there exists significant interconnectivitywithin the combined set of genes based on the published literature, thenthe genes are placed into the preliminary set of genes that comprise thepharmacogenomic network for the drug of interest. Any genes not forminga connected network are discarded as non-candidate genes for thepharmacogenomic network (block 320).

Knowledge-Based Revision of the Preliminary Pharmacogenomic Network ofDrug-Specific Interconnected Genes

Then at block 322, manual, semi-automated or automated curation, or acombination thereof, is performed on each gene in this gene setcomprising the preliminary drug pharmacogenomic network to remove geneswhose function are not related to the drug of interest in the celland/or tissue types in which it acts, or to add other genes not part ofthis preliminary set of the drug pharmacogenomic network should be addedto the set if they are judged to be specifically impacted by the drug ofinterest in the cell and/or tissue types in which they act. Theinterrogation steps include definition of an individual gene's function,the phenotypic consequences of mutational impairment of the gene, andthe human cells and tissues in which the gene is expressed, to see if itcan become a candidate for membership in the pharmacogenomic network ofthe specific drug of interest.

In one embodiment, these determinations can be made using a manual,semi-automated, or automated strategy, combining curation of each gene,its mutational profile, and its localization of expression within humantissues. These are enabled by a variety of web-based search tools,including gene definitions, genome browser annotations, the GWAScatalogue and other bioinformatics resources. For example, applicationprogramming interfaces (APIs) may have executables written in R, Python,PERL or other programming languages to facilitate data access, datacleansing and data analysis. This embodiment is an enhanced model ofmanual curation but can become time limiting if there are many geneswithin a gene set of the drug pharmacogenomic networks or the genesubsets of the sub-networks, and especially in cases where functionalgenomic elements may include regulatory RNAs or functional RNAs such aslong noncoding RNAs, or if the function of the genes are poorlyunderstood. Listing and analysis of the mutational landscape of a givengene (±10 Kb upstream and downstream) is the easiest of the 3interrogation steps to be performed because these databases are the mostcomprehensive. Other resources exist for the analysis of the tissuedistribution of a gene's expression pattern. In cases where thesepatterns are compared to sites where the specific drug of interest acts,the results from imaging modalities may be analyzed including fromradiological studies, light microscopic analysis in pathology and evenmore sophisticated methods. In some embodiments, this analysis isperformed using machine learning techniques, such as neural networks.

In another embodiment, a Bayesian probabilistic classifier may be used,either based on machine learning or using Bayesian probabilisticcomputing. The automated methods can be used to reduce the complexity ofdata analyzed from disparate data resources in which a gene's functionknowledge profile, its mutational landscape and its tissue expressionmapping are inputs to a learning machine that has been trained on anumber of such instances and tested independently on another set ofinstances for determination of accuracy. Predictive features selected bythe trained neural network can be implemented on a support vectormachine classifier to construct a gene's function and mutationalprediction model, where subsequent machine states determine the adequacyof statistical fit to the drug pharmacogenomic network.

In some scenarios, machine learning is subject to over-fitting,outputting false positives or false negatives. In another embodiment,semi-automated and naïve Bayesian classification may be performed usingmachine learning in parallel to sharpen the accuracy of the finaloutput.

Knowledge-based curation may be performed with the following steps.First, the gene definition is examined from multiple databases tounderstand if it is specifically, but not generically, impacted by thedrug of interest. In addition, the published literature, including textword strings containing the gene name or precursor gene name orequivalent protein name plus any function related to the drug ofinterest is evaluated following thorough internet searches using forexample, Google Scholar™ and/or PubMed. These may include bindingaffinity studies which have reproducibly found molecules which bind withan affinity that is within 10-fold that of the affinity of which thedrug of interest binds to the same pharmacodynamic target. Second, thedrug and dose decision server 102 examines each gene for all mutations,including SNPs, variable number of tandem repeats, duplications and allother known mutational alterations, extending in linear sequence±10 kbfrom the transcription start site(s) and stop codon(s) of the gene asexamined in a genome browser such as the UCSC genome browser or theEnsembl genome browser. If any of these mutations are found in eitherthe published literature or sources such as unpublished clinical trialdata, and they are involved in the action of the drug of interest,including efficacy, adverse events or first pass metabolism, then theyare added to the preliminary set of genes comprising the pharmacogenomicnetwork (block 324). Third, especially for complex tissues such as thebrain, skin and the cardiovascular system, the drug and dose decisionserver 102 performs concordance mapping qualitatively to compare theexpression of all genes in this final set to where the drug of interestexerts its action, if known. Genes whose expression does not match thepharmacodynamic substrate of the drug of interest are discarded (block324). Finally, third-party software such as Ingenuity Pathway Analysis™is used for examination of connectivity of this gene set (block 326).Using Fisher's right-sided exact test, if the drug and dose decisionserver 102 determines that there exists significant interconnectivitybased on the published literature, then they are placed into thepreliminary set of genes that comprise the pharmacogenomic network forthe drug of interest. Any genes not forming a connected network arediscarded as non-candidate genes for the drug pharmacogenomic network(block 328).

Iterative Gene Set Optimization

As shown at block 330, and in more detail in FIG. 10, iterative gene setoptimization is performed on the identified set of candidate genes inthe pharmacogenomic network for the particular drug of interest. Anexample method 400 for iterative gene set optimization to deconstruct adrug pharmacogenomic network into sub-networks is illustrated in theflow diagram of FIG. 10. Iterative gene set optimization may beperformed to identify sub-networks of the drug pharmacogenomic network.More specifically, iterative gene set optimization includes convertingall input molecule terms into gene or long non-coding RNA names from forexample, the Human Gene Nomenclature Committee (HGNC) names (block 402)using their API. The iterative gene set optimization differs from geneset enrichment methods, by not only combining a variety of statisticalmethods, but also not acting in a hierarchal manner ranking genes as inthreshold-dependent methods, and iterative gene set optimization doesnot rely on comparisons of experimental results, such as inwhole-distribution tests. Instead, the iterative gene set optimizationgroups genes or long noncoding RNAs from the pharmacogenomicspharmacogenomic network (block 404) using the Jaccard distance to firstmeasure the similarity between two genes or long noncoding RNAs based onthe dissimilarity of user-selected terms, where the Jaccard distance isas the ratio of the size of the symmetric difference Gene A Δ GeneB=A∩B−A∪B to the union (block 406). This is extensible into clusters ofrelated dissimilar gene names. The drug and dose decision server 102then automatically sorts these sets, or using user-defined numbers ofclusters, into subsets of clustered subsets of functionally relatedgenes using a minimal entropy sorting algorithm, such as the COOLCATalgorithm (block 408). Following gene subset optimization using entropyminimization, manual curation may be employed to assign efficacy,adverse event or functional mechanistic sub-networks based on knownattributes of the drug's mechanism of actions under consideration(blocks 410, 412).

Post-Hoc Validation Using Third Party Bioinformatics Tools

For scientific validation of the deconstruction of the drugpharmacogenomic network into mechanistic sub-networks based onfunctional gene subset optimization, each drug pharmacogenomic network'ssub-network is assessed post hoc for top Gene Ontology terms (molecularfunction and biological processes), top canonical pathways for example,as determined using other proprietary or open source pathway analysissoftware, disease risk gene variant analysis for example, as determinedusing other proprietary or open source pathway analysis software, anddetermination of upstream xenobiotic regulators using differentbioinformatics resources (block 332). In addition, the GWAS catalogue ofthe European Bioinformatics Institute, the National Human GenomeResearch Institute, and the National Institutes of Health may besearched to find significant SNP-trait associations for each gene of thegene sets for each sub-network. By providing examples of SNPs from GWASthat are statistically significant, additional evidence may be providedthat mutational impairment of the genes included in each sub-networkprovides insight into the normal, unimpaired function of thesub-network.

In some embodiments, after post hoc validation is performed, as shown inFIG. 11, the resulting pharmacogenomic network and constituentsub-networks for the particular drug of interest are stored for example,in the database 154 as shown in FIG. 1A.

For example, to map causal SNPs that discretize response to ketamine inhuman populations, their target genes within their TADs, and thepharmacogenomic contactome of these TADs, Hi-C chromosome conformationcapture data may be used from publicly-available datasets that weremapped in an A735 astrocyte cell line, H1 neuronal cell line, SK-N-SHcell lines and in samples from a postmortem human brain.

FIG. 12 shows an example of a comparison of the results of eightdifferent algorithms testing the predicted causality of the GWAS SNPrs12967143-G, an intragenic enhancer located in the TCF4 gene, a memberof the ketamine pharmacogenomic efficacy sub-network, versus other GWASSNPs, as described using the numerical output from the machine learningalgorithms used in the analysis (*p≤0.05; ** p≤0.01; ANOVA).

Using H-GREEN, a user-adjustable binning software package, thetrans-TADs are mapped that overlap between different data sources. Thetop 3 trans-TAD pharmacogenomic contacts genomewide may be selected foreach originating causal SNP TAD locus. Using prior knowledge of ketamineas an anesthetic and analgesic, and in more recent studies and clinicaltrials of ketamine and other glutamate receptor modulators asantidepressants, the methods described herein may be used to score thetop trans-TAD contacts, and for each causal SNP, the top 3 may beselected for inclusion in the pharmacogenomic network. The recentavailability of databases of validated enhancers and their targets maybe used for both originating and targeted TADs in this workflow toreconstruct the ketamine pharmacogenomic network.

The intra-TAD and trans-TAD gene sets may serve as seeds to initiatepathway analysis. Filters and thresholds may be applied that eliminategenes expressed in the cell types, neurons and astrocytes, and in brainregions where ketamine exerts it mechanism(s) of action. These do notjust include PD genes, but also PK genes, the latter which have recentlybeen shown to be expressed at high levels in relevant human brainregions where ketamine acts, and in the case of the CYP2B6 gene, areinduced by this psychotropic drug to much higher levels of expressionthan in the liver, gastrointestinal tract or kidney.

Following output of the automated pathway analysis, the drugpharmacogenomic network gene set is evaluated for plausibility, andgenes may be added to the pathway that were not selected by the pathwayanalysis program. From earlier studies of binding affinities usingmethods in molecular pharmacology, genes are added back whose productsexhibit within a 10-fold affinity of the racemic R, S-ketamine or theenantiomers to the NMDAR, as well as demonstrating molecularinter-connectivity. Other expression studies and research of themetabolism of ketamine may yield additional genes that are added back tothe ketamine pharmacogenomic network.

The ketamine pharmacogenomic network is analyzed by gene setoptimization into 3 sub-networks, of which 2 are significantly differentsub-sets of genes and regulatory RNAs using iterative analysis. The 3sub-networks include: (1) antidepressant efficacy and neuroplasticity,(2) glutamate receptor signaling, chromatin remodeling, and adverseevents, and (3) pharmacokinetics and hormonal regulation associated withthe drug. The second sub-network (2) glutamate receptor signaling,chromatin remodeling, and adverse events may include two separatesub-networks: a chromatin remodeling sub-network and a drugpharmacodynamics adverse events sub-network. To understand and validatethe pharmacogenomic network and its mechanistic sub-networks, four typesof additional analyses are performed. First, genes in thepharmacogenomic network and in each sub-network are interrogated for thepresence of enhancer SNPs that are associated with pertinent traits inGWAS. Second, pathway enrichment including biological processes andmolecular function are performed using the Gene Ontology standard todetermine the most significant top pathways for these gene sets. Third,disease gene risk variant analysis is performed, which analyzes eachgene super-set and subset for significance of the entire mutationalcontribution of these sets in humans for appropriate assignment to boththe parent pathway and its constituent sub-networks and assigns the topdiseases for super-set and sub-network set (most significant, Fisher'sexact test). Fourth, the top (most significant, Fisher's exact test)xenobiotic drug that regulates the super-set of genes comprising thepharmacogenomic network is determined. In the last case, thepharmacogenomic network set of genes should be regulated by the drugthat mediates the mechanism of the pharmacogenomic network, but for someof the sub-networks, depending on the mechanistic attributes of thatnetwork, drugs more relevant to that specific sub-network of thepharmacogenomic network mechanisms may be most significantly associated.

FIG. 13 shows the top Gene Ontology terms of the 2 significantlydifferent ketamine pharmacogenomic sub-networks in the human brain. Morespecifically; FIG. 13A shows the sub-network that mediates efficacy andneuroplasticity. FIG. 13B shows the ketamine sub-network that mediatesglutamate receptor signaling and adverse events in the human CNS.

FIG. 14A illustrates graphical depictions of the ketaminepharmacogenomic sub-network in the human brain that mediates efficacyand neuroplasticity. FIG. 14B illustrates graphical depictions of theketamine pharmacogenomic sub-network in the human brain that mediatesglutamate receptor signaling and adverse events in the human brain. Morespecifically, as shown in FIGS. 14A and 15, the genes and regulatoryRNAs located in the ketamine efficacy and neuroplasticity sub-networkinclude one or more of: Activity regulated cytoskeleton associatedprotein (ARC) gene, Achaete-Scute family bHLH transcription factor 1(ASCL1) gene, Brain derived neurotrophic factor (BDNF) gene, BDNFantisense RNA (BDNF-AS) gene, Calcium/calmodulin dependent proteinkinase II alpha (CAMK2A) gene, Cyclin dependent kinase inhibitor 1A(CDKN1A) gene, cAMP responsive element modulator (CREM) gene, Cut likehomeobox 2 (CUX2) gene, DCC netrin 1 receptor (DCC) gene, Dopaminereceptor D2 (DRD2) gene, Eukaryotic translation elongation factor 2kinase (EEF2K) gene, Fragile X mental retardation 1 (FMR1) gene,Ganglioside induced differentiation associated protein 1 like 1(GDAP1L1) gene, Glutamate metabotropic receptor 5 (GRM5) gene, Homerscaffold protein 1 (HOMER1) gene, 5-hydroxytryptamine receptor 1B(HTR1B) gene, 5-hydroxytryptamine receptor 2A (HTR2A) gene, Kruppel likefactor 6 (KLF6) gene, Lin-7 homolog C, crumbs cell polarity complexcomponent (LIN7C) long noncoding RNA, LOC105379109 long noncoding RNA,Myocyte enhancer factor 2D (MEF2D) gene, Myosin VI (MYO6) gene, Myelintranscription factor 1 like (MYT1L) gene, Neuronal differentiation 1(NEUROD1) gene, Neuronal differentiation 2 (NEUROD2) gene, Nescienthelix-loop-helix 2 (NHLH2) gene, Neuromedin B (NMB) gene, NMDA receptorsynaptonuclear signaling and neuronal migration factor (NSMF) gene,Neurotrophic receptor tyrosine kinase 2 (NTRK2) gene, Phosphotase andtensin homolog (PTEN) gene, Prostaglandin-endoperoxide synthase 2(PTGS2) gene, Rac family small GTPase 1 (RAC1) gene, Ras proteinspecific guanine nucleotide releasing factor 2 (RASGRF2) gene, Rashomolog family member A (RHOA) gene, Roundabout guidance receptor 2(ROBO2) gene, RP11_360A181 long noncoding RNA, Semaphorin 3A (SEMA3A)gene, SH3 and multiple ankyrin repeat domains 1 (SHANK1) gene, SH3 andmultiple ankyrin repeat domains 2 (SHANK2) gene, SH3 and multipleankyrin repeat domains 3 (SHANK3) gene, Solute carrier family 22 member15 (SLC22A15) gene, Solute carrier family 6 member 2 (SLC6A2) gene, Slitguidance ligand 1 (SLIT1) gene, Slit guidance ligand 2 (SLIT2) gene,Synaptosome associated protein 25 (SNAP25) gene, Synapsin I (SYN1) gene,Synapsin II (SYN2) gene, Synapsin III (SYN3) gene, T-box, brain 1 (TBR1)gene, or Transcription factor 4 (TCF4) gene.

Also, as shown in FIGS. 14B and 16, the genes and regulatory RNAslocated in the ketamine glutamate receptor signaling and adverse eventsub-network include one or more of: Acetylcholinesterase (ACHE) gene,Activating transcription factor 7 interacting protein (ATF7IP) gene,Activating transcription factor 7 interacting protein 2 (ATF7IP2) gene,ATPase Na+/K+ Transporting Subunit Alpha 1 (ATP1A1) gene, BLOC-1 relatedcomplex unit 7 (BORCS7) gene, Bromodomain containing 4 (BRD4) gene,Calcium voltage-gated channel subunit alpha1 C (CACNA1C) gene, Calciumvoltage-gated channel auxiliary subunit beta 1 (CACNB1) gene, Calciumvoltage-gated channel auxiliary subunit beta 2 (CACNB2) gene, Calciumvoltage-gated channel auxiliary subunit gamma 2 (CACNG2) gene,Cholinergic Receptor Muscarinic 2 (CHRM2) gene, Cholinergic ReceptorNicotinic Alpha 3 Subunit (CHRNA3) gene, Cholinergic Receptor NicotinicAlpha 5 Subunit (CHRNA5) gene, Cholinergic Receptor Nicotinic Alpha 7Subunit (CHRNA7) gene, Cannabinoid receptor 1 (CNR1) gene, Disks largehomolog 3 (DLG3) gene, Disks large homolog 4 (DLG4) gene, DNAMethyltransferase 1 (DNMT1) gene, Euchromatic histone lysinemethyltransferase 1 (EHMT1) gene, Gamma-aminobutyric acid type Areceptor alpha2 subunit (GABRA2) gene, Gamma-aminobutyric acid type Areceptor alpha5 subunit (GABRA5) gene, Glutamate decarboxylase 1 (GAD1)gene, Glycine receptor alpha 1 (GLRA1) gene, Glycine receptor alpha 2(GLRA2) gene, Glycine receptor beta (GLRB) gene, Glutamate ionotropicreceptor AMPA type subunit 1 (GRIA1) gene, Glutamate ionotropic receptorAMPA type subunit 2 (GRIA2) gene, Glutamate ionotropic receptor AMPAtype subunit 2 (GRIA4) gene, Glutamate ionotropic receptor NMDA typesubunit 1 (GRIN1) gene, Glutamate ionotropic receptor NMDA type subunit2A (GRIN2A) gene, Glutamate ionotropic receptor NMDA type subunit 2B(GRIN2B) gene, Glutamate ionotropic receptor NMDA type subunit 2C(GRIN2C) gene, Glutamate ionotropic receptor NMDA type subunit 2D(GRIN2D) gene, Glutamate ionotropic receptor NMDA type subunit 3A(GRIN3A) gene, Glutamate ionotropic receptor NMDA type subunit 3B(GRIN3B) gene, Hyperpolarization Activated Cyclic Nucleotide GatedPotassium Channel 1 (HCN1) gene, Histone deacetylase 5 (HDAC5) gene,Methyl-CpG binding domain protein 1 (MBD1) gene, M-Phase Phosphoprotein8 (MPHOSPH8) gene, Neural cell adhesion molecule 1 (NCAM1) gene, Nitricacid synthase 1 (NOS1) gene, Nitric acid synthase 2 (NOS2) gene, Nitricacid synthase 3 (NOS3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1)gene, Opioid receptor kappa 1 (OPRK1) gene, Opioid receptor mu 1 (OPRM1)gene, Roundabout guidance receptor 2 (ROBO2) gene, SET domain bifurcated1 (SETDB1) gene, SH3 and Multiple Ankyrin Repeat Domains 2 (SHANK2)gene, Sigma Non-Opioid Intracellular Receptor 1 (SIGMAR1) gene, Solutecarrier family 6 member 9 (SLC6A9) gene, Transcription ActivationSuppressor (TASOR) gene, TOG array regulator of axonemal microtubules 2(TOGORAM2) gene, Tripartite Motif Containing 28 (TRIM28) gene, or ZincFinger Protein 274 (ZNF274) gene.

As shown in FIG. 17, the genes and regulatory RNAs located in thepharmacokinetic enzymes and hormones sub-network include one or more of:Anaphase promoting complex subunit 2 (ANAPC2) gene, Cytochrome P450family 2 subfamily A member 6 (CYP2A6) gene, Cytochrome P450 family 2subfamily B member 6 (CYP286) gene, Cytochrome P450 family 3 subfamily Amember 4 (CYP3A4) gene, Disks large homolog 4 (DLG4), EukaryoticElongation Factor 2 Kinase (EEF2K) gene, Estrogen Receptor 1 (ESR1)gene, Glutamate ionotropic receptor AMPA type subunit 1 (GRIA1) gene,Glutamate ionotropic receptor AMPA type subunit 2 (GRIA4) gene,Glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) gene,Glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B) gene, MyosinVI (MYO6) gene, Roundabout Guidance Receptor 2 (ROBO2) gene, SH3 andMultiple Ankyrin Repeat Domains 2 (SHANK2) gene, and TranscriptionElongation Regulator 1 (TCERG1) gene.

Automated iterative gene set optimization of the psychotropicpharmacogenomic network into sub-networks may be user-limited toinvestigate other features of the pharmacogenomic network. As describedabove with reference to FIG. 10, the ketamine pharmacogenomic network isiteratively deconstructed until certain specific genes do notsignificantly associate with any sub-network or were associated withseveral sub-networks deconstructed from the drug pharmacogenomicnetwork. The gene ESR1 encodes the nuclear hormone receptor for estrogenand it regulates the expression of several genes in the ketaminepharmacogenomic network. It is well known that estrogen greatly inducesthe CYP286 gene both in the human brain and elsewhere.

The learning architecture for training the pattern matching sub-networksincludes pre-training the reference set (ref. no. 710). Morespecifically, at block 704, the drug and dose decision server 102develops the patient's pattern matching sub-networks derived from thepatient input biosample, and co-develops separate trained patternmetrics (block 712), which contain the features of the efficacy andadverse event sub-networks, to a joint feature representation metric.For determination of similarity to the reference set (blocks 706, 708),the two different pairs of reference-patient metrics include an accuratemeasurement of similarity and outputs similarity scores for each of theefficacy and adverse events (blocks 714, 716). At block 702, thebiosample obtained from a patient, which may be a cheek swab, saliva,blood or urine sample, undergoes targeted enhancer SNP genotyping, aswell as combined chromosome conformation capture and RNA-seq. Then atblock 704, the drug and dose decision server 102 performs analysisnecessary to build the input patient-specific map of efficacy andadverse event sub-networks for a specific drug of interest. Thesepatient-specific, drug-induced sub-network patterns could be furtherprocessed using Bayesian probabilistic computing to fill in sparse ormissing data. As a new patient enters as an input, the pretrainedreference set of drug-specific efficacy and adverse event sub-networksfor pattern matching is once again optimized for subsequent patients,producing a more accurate measure of pharmacogenomic variability amonghumans with enhanced clinical utility. This matching task assumes thatpatches go through the same feature encoding before computing andoutputting a similarity score, greatly increasing efficiency whilereducing computational requirements.

Each set of inputs (reference set (ref. no. 710) and patient set (ref.no. 720)) are thus constructed differently with feature set extractionand inference of sparse data using probabilistic computing based onBayesian distribution to increase the accuracy of reference and patientmaps. The trained feature network is based on a “Siamese” networkapproach, with the constraint that the two sets must share the sameparameters. When completed, the patient's drug-induced trained patternnetworks are coupled with those obtained from the reference database,pairing efficacy feature set pairs and adverse event feature set pairs.These provide the basis for the development of a trained efficacy metricand a trained adverse event metric that attempt to match all of thefeatures from the patient and the reference set for the drug ofinterest. These pairwise matching scores yield separate efficacy andadverse event similarity scores between reference and patient.

In a further embodiment, a reference pattern matching set may bedeveloped for each patient that could be used to create apatient-specific database of such reference maps, and updated in aperiodic manner as additional biosamples are obtained from the patientin a longitudinal manner, obtained in a clinical setting or outpatientpharmacy over time.

In any event, the drug and dose decision server 102 may then use thesimilarity scores for the efficacy and adverse event sub-networks forthe psychotropic drug of interest, generated via the method 700, todetermine whether to administer the psychotropic drug of interest to thepatient. For example, the similarity score for the efficacy sub-networkmay be compared to a threshold similarity score. If the similarity scorefor the efficacy sub-network is above the threshold similarity score,the drug and dose decision server 102 may determine that thepsychotropic drug of interest should be administered to the patient. Thesimilarity score for the adverse event sub-network may also be comparedto a threshold similarity score. If the similarity score for the adverseevent sub-network is below the threshold similarity score, the drug anddose decision server 102 may determine that the psychotropic drug ofinterest should be administered to the patient. In another embodiment,the similarity scores for the efficacy sub-network and the adverse eventsub-network may be combined or aggregated in any suitable manner. Forexample, the similarity score for the adverse event sub-network may besubtracted from the similarity score for the efficacy sub-network. Ifthe combined score is greater than a threshold similarity score, thedrug and dose decision server 102 may determine that the psychotropicdrug of interest should be administered to the patient.

Otherwise, the drug and dose decision server 102 may determine not toadminister the psychotropic drug of interest, and may provide arecommendation to the health care professional's client device 106-116to administer another drug to treat the patient's depression.

In some embodiments, the drug and dose decision server 102 may determinewhether to administer the psychotropic drug of interest to the patientby generating a machine learning model based on training data from drugresponses from patients previously prescribed the psychotropic drug ofinterest. The machine learning model may be generated based on severalcharacteristics of the previous patients including drug-inducedsub-networks for the previous patients, PD and PK SNPs that stratifypatients by drug response, neuroimaging data, direct TAD-specificmeasures including differential gene expression, and clinical variablesfor the previous patients such as age, weight, biological sex, body massindex, ethnicity, family history, patient history of substance abuse,diagnostic codes, hospitalization history, drug-drug interactions,mental illness history, whether the patient smokes or uses nicotine, andHamilton Scale for Depression (HAM-D) score. The drug and dose decisionserver 102 may obtain the same characteristics for a current patientincluding molecular and clinical data and apply the current patient'scharacteristics to the generated machine learning model to determinewhether to administer the psychotropic drug of interest to the patient.

In addition to determining whether to administer the psychotropic drugof interest to the patient, the drug and dose decision server 102determines the dosage to administer to the patient. FIGS. 18A and 18Billustrate an analysis performed on an independent cohort dataset forthe development of a regression model for the dose estimation ofketamine. The published literature and other sources have provided bothpharmacokinetic SNP data and clinical values that help determine dosebased on CYP2B6 SNPs and clinical data. As can be seen from thedifferential analysis, the biggest contribution to ketamine dose is thepresence or absence of the poor metabolizer phenotype, rs3745274, avariant that causes exon skipping and loss of the hyper-inducible CYP2B6first pass metabolism of ketamine. The range of quantitative intra-nasalketamine doses in this model derivation cohort was 0.4 to 0.8 mg/kg. Thedrug and dose decision server 102 may generate the model based onpublished ketamine clinical trial results obtained fromclinicaltrials.org. A generic example of summed values based onregression variables that could be included in the ketamine-dosingalgorithm is illustrated in the equation below:

Dose=exp [2.00×rs3745274+0.25×female (biologicalsex)+0.22×rs3786547+0.22×clopidogrel+0.19×rs11083595+0.11×BSA+0.20×smokes+0.17×suicideattempt history+0.07×age per decade+0.09×Non-Hispanic whiteethnicity+020×ticlopidine+0.15×previous psychiatric hospitalization]

More specifically, the drug and dose decision server 102 may generatethe dosing algorithm based on published literature and may havepredetermined constants to apply to each of several patientcharacteristics, such as biological characteristics, demographiccharacteristics, clinical characteristics, etc., as in the equationabove. In other embodiments, the drug and dose decision server 102 maygenerate the dosing algorithm using machine learning techniques. Forexample, the drug and dose decision server 102 may collect dosinginformation on patients previously prescribed ketamine as training data.The dosing information may include the dosage each patient wasprescribed along with indications of whether the patient's dosage wasadjusted during treatment and/or whether the patient experienced adverseevents. The drug and dose decision server 102 may then analyze thetraining data to generate a machine learning model (e.g., a neuralnetwork, a decision tree, a hyperplane, a regression model, etc.) todetermine the dosage for a new patient based on the new patient'sbiological characteristics, demographic characteristics, and clinicalcharacteristics. The patient characteristics utilized in the dosingalgorithm may include biological data, such as SNPs that have beenreported to stratify response to ketamine in humans. The patientcharacteristics may also include demographic data for the patient, suchas the patient's sex, height and weight, age, and ethnicity.Furthermore, the patient characteristics may include clinical data, suchas family history, drug-drug interactions, mental illness history,whether the patient smokes or uses nicotine, and Hamilton Scale forDepression (HAM-D) score.

In any event, the drug and dose decision server 102 applies thepatient's characteristics to the dosing algorithm to determine a dosageof ketamine to administer to the patient. Then the drug and dosedecision server 102 provides the recommended dosage to a health careprofessional's client device 106-116.

Although regression analysis for patient-specific dose optimizationcannot account for almost half of the pharmacogenomic and clinicalvariables required for accuracy, a few published studies have reportedvariables to include for algorithmic determination of antidepressantselection and dose estimation. Clinical values obtained from a medicalrecord are also critical for determining decreases in the dosing ofketamine, as shown in FIG. 13. These include a body mass index (BMI)over 30, a family history of alcohol use disorder (first degree), ahistory of suicide attempt(s), previous psychiatric hospitalization, afemale biological sex (pre-menopausal), non-Hispanic white ethnicity,and individuals who smoke tobacco. As indicated, these values contributeto ketamine dosing that can be substantive, but do not rule out use ofthis medication.

In addition to comparing the patient's sub-networks for the psychotropicdrug of interest to reference sub-networks for the psychotropic drug ofinterest, the drug and dose decision server 102 analyzes clinical dataand neuroimaging data for the patient to determine whether to administerthe drug to the patient. For example, the drug and dose decision server102 may analyze the patient's HAMD score and/or patient symptoms tocategorize the patient into one of four TRD patient subtypes.

FIG. 19 shows the four TRD patient subtypes as determined bytranscranial magnetic stimulation (TMS) coupled with neuroimaging ofresting connectivity network in human brain. In patients with TRD,subtypes have been identified and replicated using transcranial magneticstimulation (TMS) and neuroimaging studies, which have discretized thesubtypes by differential activation and repression of the resting stateconnectivity network in human brain. TMS has four different anatomicalplacements on the outside of the human head that activate differentneuroanatomical structures that are part of limbic-cortical circuits,and include parts of the resting state connectivity network, the defaultmode network, the defense response that involves the amygdala, rewardcircuits located in the basal forebrain including the nucleus accumbens(NA) or orbitofrontal cortex (OFC) in depression subtype 3, gating ofsensory stimuli to the cortex through the thalamus, cortical region S1and the insula, prefrontal cortical restraint of impulsivity involvingthe dorsolateral and dorsomedial prefrontal cortex inhibition of theamygdala activation of limbic cortex, including the entire anteriorcingulate cortex and memory consolidation in the hippocampal formation.

FIG. 19 also illustrates that TRD subtypes can be identified usingclinical data obtained from an EHR or other clinical records, eitherusing structured values or from notes using natural language processing.

FIG. 20 shows regions of the brain that are consistently activatedduring the ketamine antidepressant response and how they aredifferentially mapped to the four TRD depression subtypes. Althoughbrain regions are involved, these are consistent with both the differentdepression subtypes as defined by TMS and clinical values, as well asresults from the neuroimaging studies, which have examinedketamine-induced activation and suppression.

FIG. 21 provides recommendations for medication switching from ketaminefor the 4 different TRD subtypes based on defining HAMD-17 rating(Hamilton Scale of Depression), the neuroimaging meta-analysis,associated clinical values as shown in FIG. 19, and availableinformation concerning the efficacy, indications and recommendations forcurrently available antidepressants and professional guidelines from theAmerican Psychiatric Association.

FIG. 21 illustrates example drug recommendations and alternativemedication options for each of the four different subtypes of TRDdepressed patients.

To identify which depression subtypes should or should not be providedwith ketamine and ketamine analogs, 24 publicly available neuroimagingdatasets are analyzed to determine the neuroanatomical regions that areactivated by ketamine and its analogs, depressed and TRD patients andhealthy controls. Since TRD subtype 3 patients consistently exhibithyperactive sub-geniculate anterior cingulate cortex (sgACC),dorsolateral and dorsomedial prefrontal (executive) cortices (dIPFC,dmPFC) and hyperactive orbitofrontal cortex (OFC), it is recommendedthat these patients do not receive ketamine pharmacotherapy because thispatient cohort will not respond or remit, but may instead experienceexaggerated psychotropic adverse drug events. Independent analysis ofthe neuroanatomical localization of all of the genes found in theketamine pharmacogenomic network shows that they are all expressed athigh levels in the anterior cingulate gyrus, prefrontal cortex,supplementary motor cortex, orbitofrontal cortex, temporal cortex,amygdala, hippocampal formation, anterior caudate and nucleus accumbens,but not in other cortical brain regions, hypothalamus or brainstem. Thisis identical to the pattern of 24 functional neuroimaging studies thatwere examined showing where ketamine first acts in human brain to exertits antidepressant action (Table 1).

TABLE 1 Functional neuroimaging studies demonstrate the human CNSsubstrate where ketamine and other NMDAR modulators act, and in whichall of the genes in the ketamine pharmacogenomic network are expressedat their highest levels. TITLE PARTICIPANTS KETAMINE’S SITE(S) OF ACTIONMOD. PMID Effects of sub-anesthetic 9 healthy Anterior cingulate cortex(ACC) and PET 12960545 doses of ketamine on controls prefrontal cortex(PFC). Not recognized - regional cerebral blood flow, anterior caudate(AC) and nucleus oxygen consumption, and accumbens (NA). blood volume inhumans Effects of ketamine on 10 healthy Anterior cingulate cortex(ACC). 1H- 15677610 anterior cingulate glutamate controls MRS metabolismin healthy humans: a 4-T proton MRS study Increased anterior cingulate11 healthy Patients with MDD exhibited increased MEG 18822408 corticalactivity in controls and 11 activity in the anterior cingulate cortexresponse to fearful faces: A drug-free (ACC) after pretreatment withfearful neurophysiological patients faces versus controls. Also, changesbiomarker that predicts diagnosed with observed in right amygdala (AMY).rapid antidepressant MDD response to ketamine Anterior cingulate 15drug-free Subgenual anterior cingulate cortex MEG 20393460desynchronization and patients (ACC), supplementary motor area (SMA)functional connectivity with diagnosed with and amygdala (AMY). theamygdala during a MDD working memory task predict rapid antidepressantresponse to ketamine Ketamine decreases resting 17 healthy Subgenualanterior cingulate cortex fMRI 23049758 state functional networkcontrols (sgACC), dorsomedial prefrontal cortex BOLD connectivity inhealthy (PFC), subjects: Implications for antidepressant drug actionRelationship of resting brain 22 healthy Ketamine administrationincreased rs- 23337947 hyperconnectivity and controls; global brainconnectivity. fcMRI schizophrenia-like Replication in Psychotomimetic(negative) effects symptoms produced by the another 12 followingketamine administration were NMDA receptor healthy controls localized tothe nucleus accumbens (NA) antagonist ketamine in and anterior caudate(AC). Positive humans symptoms were associated with changes inprefrontal cortex (PFC), supplementary motor area (SM), insula andposterior cortex. Neural correlates of rapid 20 drug-free Anteriorcingulate cortex (ACC), PET 23540908 antidepressant response to patientsprefrontal cortex (PFC), amygdala (AMY) ketamine in treatment- diagnosedwith and habenula. resistant unipolar TRD depression: A preliminary PETstudy Neural correlates of rapid 21 patients with Sub-geniculateanterior cingulate cortex PET 24103187 antidepressant response tobipolar (ACC), supplementary motor area ketamine in bipolar disorderdepression (SMA), prefrontal cortex (PFC). Also, unrecognized, butincluded amygdala (AMY), hippocampal formation (HF), anterior caudate(AC) and nucleus accumbens (NA). Anti-anhedonic effect of 36 patientswho Ketamine significantly activated the PET 25313512 ketamine and itsneural are treatment- dorsal anterior cingulate cortex (dACC) correlatesin treatment- refractory and the subcortex. resistant bipolar depressiondiagnosed with bipolar disorder I or II Neural correlates of change 52patients Supplementary motor area (SMA), PET 25691504 in majordepressive disorder diagnosed with hippocampal formation (HF), frontalanhedonia following open- TRD gyrus and orbitofrontal cortex correlatedlabel ketamine. with measures of decreased anhedonia in patientsdiagnosed with MDD. Borderline significance. A pilot in vivo proton 11patients Prefrontal cortex (PFC). 1H- 26283639 magnetic resonancediagnosed with MRS spectroscopy study of amino MDD acid neurotransmitterresponse to ketamine treatment of major depressive disorder The effectsof low-dose 48 patients Prefrontal cortex (PFC), amygdala PET 26821769ketamine on the prefrontal diagnosed with (AMY) and supplementary motorarea cortex and amygdala in TRD (SMA). treatment-resistant depression: Arandomized controlled study Ketamine modulates 13 healthy Followingketamine infusion, largest fMRI 26925332 subgenual cingulate controlschanges observed in the connectivity of connectivity with the thesubgenual anterior cingulate cortex memory-related neural (sgACC).circuit-a mechanism of relevance to resistant depression? Comparing theactions of 60 un-medicated Intravenous infusion of both ketamine phMRI27133029 Lanicemine and ketamine in patients and Lanicemine graduallyincreased depression: key role of diagnosed with activity in thesubgenual anterior the anterior cingulate MDD cingulate cortex (sgACC).Ketamine modulates 15 healthy Dorsomedial prefrontal cortex (PFC) andrs- 27480949 hippocampal controls anterior cingulate cortex (ACC).fcMRI; neurochemistry and Psychosis severity produced by 1H- functionalconnectivity - A ketamine was associated with increased MRS combinedmagnetic connectivity of the hippocampal resonance spectroscopyformation (HF) with the middle cingulate and resting state fMRI studycortex, insula, precuneus and superior in healthy volunteers frontalgyrus. Ketamine treatment and 25 healthy Subgenual anterior cingulatecortex rs- 27604566 global brain connectivity controls and 18 (sgACC),dorsolateral prefrontal cortex fcMRI in major depression drug-free anddorsomedial prefrontal cortex (PFC), patients anterior caudate (AC),nucleus diagnosed with accumbens (NA). MDD The nucleus The first cohortThe volume of the nucleus accumbens 1H- 28272497 accumbens and ketaminewas 34 patients (NA) was altered in MDD patients, while MRS treatment inmajor diagnosed with hippocampal formation (HF) volume was depressivedisorder MDD and 26 increased following ketamine in MDD healthycontrols. patients who exhibited remission. The second cohort was 16patients diagnosed with MDD. Persistent antidepressant 24 patients TRDpatients receiving the PET 28922734 effect of low dose ketaminediagnosed with 0.5 mg/kg ketamine infusion exhibited and activation inTRD activation in supplementary the supplementary motor motor area (SMA)and area and anterior cingulate anterior cingulate cortex cortex intreatment- (ACC) than did those receiving the resistant depression: 0.2mg/kg ketamine infusion. The A randomized control study increase in theSUV in the ACC was negatively correlated with depressive symptoms afterketamine infusion. Glutamate levels and resting 25 healthy Dorsomedialprefrontal cortex (PFC) and 1H- 29467681 cerebral blood flow in controlsanterior cingulate cortex (ACC). MRS anterior cingulate cortex areassociated at rest and immediately following infusion of S-ketamine inhealthy volunteers Default mode connectivity in 33 patients MDD patientsexhibited normalization of fMRI 29580569 major depressive disorderdiagnosed with connectivity between the insular cortex measured up to 10MDD and 25 (IC), posterior anterior cingulate cortex days after ketaminehealthy controls (pACC) and subgenual anterior cingulate administrationin a cross-over cortex (sgACC). study 7T 1H-MRS in major 17 healthyDifferent MDD phenotypes exhibited 1H- 29748628 depressive disorder: Acontrols and 20 different brain region alterations MRS KetamineTreatment Study patients following ketamine infusions, ranging diagnosedwith from sub-geniculate anterior cingulate MDD cortex (ACC) to anteriorcaudate (AC). Pharmacological fMRI: 17 healthy male Anterior cingulatecortex (ACC), superior rs- 30003027 Effects of sub- subjects frontalgyrus including supplementary fcMRI anesthetic ketamine on motor area(SMA), amygdala (AMY), resting-state functional hippocampal formation(HF), anterior connectivity in the default caudate (AC), nucleusaccumbens (NA), mode network, salience prefrontal cortex (PFC). network,dorsal attention network and executive control network Ketamine, but Notthe 56 un-medicated Ketamine increased global connectivity fMRI 30263977NMDAR antagonist patients of the prefrontal cortex (PFC) in Lanicemine,Increases diagnosed with depressed patients. prefrontal globalconnectivity MDD in depressed patients Functional connectivity 24patients A single sub-anesthetic dose of ketamine rs- 30819549 betweenprefrontal cortex diagnosed with increased functional connectivity fcMRIand Subgenual cingulate MDD between prefrontal Cortex (PFC) and thepredicts antidepressant subgenual anterior cingulate cortex effects ofketamine (sgACC). The antidepressant effect of 10 patients Rapid (1hour) and sustained (7 days) 1H- 21232924 ketamine is not associateddiagnosed with antidepressant effects produced by MRS with changes inoccipital MDD ketamine were not associated with amino acidneurotransmitter changes in amino acid neurotransmitter content asmeasured by content in occipital cortex (OC). [¹H]-MRSNeuroimaging modalities (MOD.):1H-MRS: 4-T 1H proton magnetic resonance spectroscopyfMRI: Functional magnetic resonance imaging.

MEG: Magnetoencephalography;

PET: FDG positron emission tomography;phMRI; Pharmacological magnetic resonance imaging.rs-fcMRI: resting-state functional connectivity magnetic resonanceimaging.

In another embodiment, disease risk and pharmacogenomic SNPs thatdiscriminate the 2 significantly different ketamine sub-networks inhuman brain may be used to determine a patient's response and adverseevents when treated with ketamine. Table 2A lists the enhancer andsuperenhancer SNPs that have been found in the ketamine efficacysub-network that may be used to determine the representation ofmutations significantly associated with efficacious response toketamine. In contrast, Table 2B lists the enhancer and superenhancerSNPs that have been found in the ketamine adverse event sub-network thatmay be used to determine the representation of mutations significantlyassociated with adverse CNS events in response to ketamine.

TABLE 2A Part 1. Enhancer and superenhancer SNPs that have been found inthe ketamine efficacy sub-network. EBI- GWAS SNP Vari- Odds ConfidenceNHGRI Reported/ Reported ant Reported P- ratio Interval PubMed AccensionPopulation(s) gene(s) type trait value or beta (95%) ID Numberrs7623659- RHOA Intro Cognitive 4.00E−57 0.0395 0.035-0.044 30038396GCST006570 T/EUR nic performance unit increase rs12229654- LINC01405-Inter Alcohol 2.00E−48 2.31 21270382 GCST004404 G/ASN CUX2 genicconsumption (drinkers versus non- drinkers) rs30266-A/ ENSG00000251574Inter Recurrent 2.00E−45 1.033 1.028-1.037 30718901 GCST007342 EUR genicdepression (F33) rs61902811- TMPRSS5, Inter Recurrent 4.00E−39 1.02880661.02-1.03 30718901 GCST007342 G/EUR DRD2 genic depression (F33)rs12229654- LINC01405- Inter Alcohol 4.00E−35 0.79 [0.67-0.91] 21270382GCST000954 G/ASN CUX2 genic consumption unit decrease rs6265-T/ BDNF,Intra Smoking 9.00E−29 0.029275492 0.023-0.036 30643251 GCST007468 EURBDNF-AS genic; status unit non- decrease synon- omous rs7227069- DCC 3′Recurrent 2.00E−28 1.024  1.02-1.029 30718901 GCST007342 A/EUR UTRdepression (F33) rs12967143- TCF4 Intra Recurrent 2.00E−27 1.0256411.02-1.03 30718901 GCST007342 G/EUR genic depression (F33) rs4938021-TMPRSS5, Inter Wellbeing, 3.00E−26 0.010079761 unit [0.0082-0.0119]30643256 GCST007341 T/EUR DRD2 genic life increase satisficationrs6589377- TMPRSS5, Inter Neuroticism, 4.00E−26 0.016052796 unit0.016052796 30718901 GCST007340 A/EUR DRD2 genic loneliness increaseunit increase rs6589377- TMPSS5, Inter Depressive 5.00E−26 0.0160527960.013-0.019 30643256 GCST007339 A/EUR DRD2 genic symptom unitmeasurement increase rs7932640- GRM5 Intra Recurrent 3.00E−25 0.44171.023 30718901 GCST007342 T/EUR genic depression (F33) rs7111031-TMPRSS5, Inter Well being, 3.00E−24 0.012320721 unit [0.0099-0.0147]30643256 GCST007338 A/EUR DRD2 genic positive increase affectrs11662271- DCC Intra Cognitive 3.00E−24 0.0233 unit [0.019-0.028]30038396 GCST006570 T/EUR genic function increase rs1373178- DCC IntraSmoking 2.00E−22 0.0107536 0.0086-0.0129 30643251 GCST007468 G/EUR genicstatus unit decrease rs1925950- MEF2D Intra Migraine 9.00E−22 1.071.06-1.09 27322543 GCST003720 G/EUR genic; non- synon- omous rs8084280-DCC Intra Depressive 3.00E−19 0.008229027 0.0064-0.01 30643256GCST007340 T/EUR genic symptom unit measurement increase rs7111031- DRD2Inter Neuroticism 2.00E−18 0.012320721 0.0099-0.0147 29255261 GCST005232A/EUR genic unit increase rs1925950- MEF2D Intra Migraine 2.00E−18 1.291.20-1.34 27182965 GCST003720 G/EUR genic; non- synon- omous rs12520354-RASGFR2- Intra Risk-taking 3.00E−17 0.0114 0.0087-0.0141 30643258GCST007325 A/EUR AS genic behavior unit increase rs599550-A/ TCF4 IntraDepressed 4.00E−17 6.53 z-score 29942085 GCST006475 EUR genic affect,mood increase disorders rs613872-C/ TCF4 Intra Loneliness 4.00E−170.022766946 0.017-0.028 29970889 GCST006924 EUR genic unit decreasers8084280- DCC Intra Wellbeing, 3.00E−16 0.010693542 unit[0.0081-0.0133] 30643256 GCST007337 T/EUR genic life increasesatisfication rs11662271- DCC Intra Increased 4.00E−16 0.0233 unit[0.019-0.028] 30018396 GCST006572 T/EUR genic cognition increasers12967143- TCF4 Intra Well being 9.00E−16 0.008096604 unit[0.0061-0.0101] 30643256 GCST007341 C/EUR genic decrease rs8181326-ARHGAP19, Inter Risk-taking 1.00E−15 0.016249152 unit [0.012-0.02] 30643258 GCST007324 A/EUR SLIT1 genic behavior decrease rs72930774-TCF4-AS2, Inter Risky sexual 2.00E−15 0.04191511 unit [0.032-0.052]30643258 GCST007326 A/EUR TCF4 genic behavior increase measurementrs2958162/ TCF4 Intra Depressive 3.00E−15 1.51 1.45-1.57 30643256GCST007339 EUR genic symptoms rs7949802- TMPRSS5, Inter Wellbeing,6.00E−15 0.011424591 unit [0.0086-0.0143] 30643256 GCST007337 T/EUR DRD2genic life decrease satisfication rs1660237- TCF4 Intra Well being,7.00E−15 0.00893267 unit [0.0067-0.0112] 30643256 GCST007338 T/EUR genicpositive increase affect rs674437-A/ GRM5 Intra Depressive 9.00E−150.008528544 [0.0064-0.0107] 30643256 GCST007340 EUR genic symptoms unitdecrease rs624244/ TCF4 Intra Risk-taking 2.00E−14 0.0095 unit[0.0071-0.0119] 30643258 GCST007325 EUR genic behavior decreasers8099160/ DCC Intra Recurrent 2.00E−14 7.638 z 29942085 GCST006477 EURgenic depression score (F33) increase rs674437-A/ GRM5 Intra Positive2.00E−14 0.010211199 unit [0.0076-0.0128] 30643256 GCST007388 EUR genicaffect decrease rs599550-A/ TCF4 Intra Feeling “fed- 3.00E−14 7.6 z29500382 GCST006947 EUR genic up” score measurement increase rs7117514-SHANK2 Intra Recurrent 4.00E−14 1.0162601 [1.01-1.02] 30718901GCST007342 G/EUR genic depression (F33) rs2163971- CADM2 Intra Riskysexual 5.00E−14 0.017939975 unit [0.013-0.023] 30643258 GCST007326 T/EURgenic behavior increase measurement rs4936277- DRD2, Inter Alcohol use1.00E−13 7.44 z 29942085 GCST008259 A/EUR TMPSS5 genic disorder, scorealcohol increase dependence rs13357015/ RASGFR2 Intra Smoking 1.00E−1330643251 GCST007468 EUR genic status rs17601612- DRD2, Inter Alcohol2.00E−13 30643258 GCST007328 C/EUR TMPSS5 genic consumption rs8084351/DCC Intra Depressive 2.00E−13 29292387 GCST005323 EUR genic symptommeasurement rs61687445- DRD2, Inter Well being 2.00E−13 0.006787939 unit [0.005-0.0086] 30643256 GCST007341 A/EUR TMPSS5 genic decreasers35738585/ DRD2, Inter Depressed 2.00E−13 0.01722 unit [0.013-0.022]29942085 GCST006475 EUR TMPSS5 genic affect decrease rs9636107- TCF4Intra Schizophrenia 1.00E−12 26198764 GCST003048 G/EUR genic rs12968428-DCC Intra Recurrent 2.00E−12 7.065 z 29942085 GCST06477 A/EUR genicdepression score (F33) increase rs8138473/ SHANK3 Intra Cognitive3.00E−12 0.0194 unit [0.014-0.025] 30038396 GCST006570 EUR genicperformance decrease rs619466-G/ TCF4 Intra Depressive 3.00E−120.010945462 unit [0.0079-0.014]  30643256 GCST007340 EUR genic symptomsincrease rs1431181- DCC Intra Recurrent 3.00E−12 6.958 z 29942085GCST06477 A/EUR negic depression score (F33) increase rs1261070-?/ TCF4Intra Unipolar 6.00E−12 0.02838 unit  [0.02-0.036] 29942085 GCST006475EUR genic depression, decrease mood disorders rs611439-?/ TCF4 IntraUnipolar 6.00E−12 0.02861 unit  [0.02-0.037] 29942085 GCST006475 EURgenic depression, decrease mood disorders rs7231748- TCF4 IntraIrratible 7.00E−12 6.87 z 29500382 GCST006941 A/EUR genic mood scoredecrease rs4277413- DCC Intra Recurrent 1.00E−11 6.77 z 29942085GCST06477 A/EUR genic depression score (F33) decrease rs1050316/ MEF2DIntra Headache 2.00E−11 0.0098 unit [0.0069-0.0127] 29397368 GCST005337EUR genic decrease rs139438618/ SEMA3A Intra Major 2.00E−11 0.869 unit29071344 GCST005022 AFR genic depression increase and alcoholismrs8089865- DCC Intra Depressed 3.00E−11 6.624 z 29942085 GCST06477 A/EURgenic affect, mood score disorders increase rs12958048- TCF4 IntraRecurrent 4.00E−11 1.03 [1.02-1.04] 29700475 GCST005839 A/EUR genicdepression (F33) rs9636107/ TCF4 Intra Autism 5.00E−11 1.0638298[1.04-1.09] 28540026 GCST004521 EUR genic spectrum disorder orschizophrenia rs17598729- TCF4 Intra Schizophrenia 9.00E−11 1.0893246[1.06-1.12] 30285260 GCST007201 C/ASN genic rs4801157/ TCF4 IntraDepressed 2.00E−10 0.01921 unit [0.013-0.025] 29942085 GCST006475 EURgenic affect, mood increase disorders rs4384683- DCC Intra Chronic back2.00E−10 1.0309278 [1.02-1.04] 30261039 GCST007152 G/EUR genic painrs674437-A/ GRM5, Inter Neuroticism, 3.00E−10 6.31 z 29500382 GCST006476EUR TYR genic vulnerability score decrease rs4938021- DRD2- InterNeuroticism, 4.00E−10 0.02327652 [0.016-0.031] 27089181 GCST003770 T/EURTMPRSS5 genic loneliness unit increase rs624244-A/ TCF4 Intra Risktaking 7.00E−10 0.01678442 unit [0.011-0.022] 30643258 GCST007323 EURgenic dependency decrease rs674437-A/ GRM5, Intra Neuroticism 8.00E−106.146 z 29942085 GCST006940 EUR TYR genic score decrease rs7228159- TCF4Intra Feeling 1.00E−09 6.046 unit 29500382 GCST006950 A/EUR genic worryincrease rs61905363- DRD2 Intra Depressive 1.00E−09 0.014688093 unit[0.0099-0.0194] 30643256 GCST007340 T/EUR genic symptom decreasemeasurement rs4936277- DRD2, Intra Depressive 2.00E−09 0.006741934 unit[0.0045-0.0089] 30643256 GCST007340 G/EUR TMPSS5 genic symptom decreasemeasurement rs17041417- ASCL1 Intra Neuroticism 2.00E−09 0.011447053unit [0.0077-0.0152] 30643258 GCST007339 A/EUR genic increasers17041417- ASCL1 Intra Depressive 2.00E−09 0.00732364 unit[0.0049-0.0097] 30643258 GCST007340 A/EUR genic symptom increasemeasurement rs17041417- ASCL1 Intra Positive 3.00E−09 0.008780573 unit[0.0059-0.0117] 30643256 GCST007338 A/EUR genic affect increasers9811546- CADM2 Intra Feeling 6.00E−09 5.83 z 29500382 GCST006952 A/EURgenic tense score decrease rs17041417- ASCL1 Intra Wellbeing, 6.00E−090.009325971 unit [0.0062-0.0125] 30643256 GCST007337 A/EUR genic lifeincrease satisfication rs12575685/ SHANK2 Intra Bipolar 8.00E−09 1.07272[1.05-1.1]  31043756 GCST008103 EUR genic disorder rs2274316- MEF2DIntra Migraine   1E−08 1.07 [1.05-1.10] 23793025 GCST002081 C/EUR genicrs775766-A/ ROBO2 Intra Recurrent 2.00E−08 5.659 z 29942085 GCST006477EUR genic depression score (F33) increase rs310763-C/ SYN2 IntraDepressive 3.00E−08 0.006332982 unit [0.0041-0.0086] 30643256 GCST007340EUR genic symptom decrease measurement rs310763-C/ SYN2 Intra Wellbeing, 3.00E−08 0.007597797 unit [0.0049-0.0103] 30643256 GCST007338 EURgenic positive decrease affect rs310763-C/ SYN2 Intra Wellbeing,3.00E−08 0.0062105684 unit  [0.004-0.0084] 30643256 GCST007341 EUR geniclife decrease satisfication rs1016306- CADM2 Intra Well being 5.00E−080.005018757 unit [0.0032-0.0068] 30643256 GCST007341 T/EUR genicdecrease rs935526-T/ ROBO2 Intra Recurrent 5.00E−08 5.452 z 29942085GCST006477 EUR genic depression score (F33) decrease rs161645-A/ENSG00000251574 Inter Depression 8.00E−08 23290196 GCST001802 EUR genic(quantitative trait) rs17211233- RASFGRF2 Intra Response to  2.00E−07*26.9757 unit [17.75-36.2]  30552316 GCST007317 T/EUR genic ketamine indecrease bipolar disorder or major depression (decrease in dissociationeffects) rs11214606/ DRD2 Intra Response to  5.00E−07* 21107309GCST000883 EUR genic antipsychotic treatment in schizophrenia (workingmemory) rs1400237/ ROBO2 Intra Response to  2.00E−06* 21.4345 unit13.27-29.6  30552316 GCST007317 EUR genic ketamine in increase bipolardisorder or major depression (increase in dissociation effects)rs1846786- ENSG00000225960 Inter Response to  3.00E−06* 13.9838 unit [8.54-19.43] 30552316 GCST007317 T/EUR genic ketamine in decreasebipolar disorder or major depression (decrease in dissociation effects)rs4855976/ ROBO2 Intra Response to  8.00E−06* 23.7477 unit [14.02-33.48]30552316 GCST007317 EUR genic ketamine in increase bipolar disorder ormajor depression (increase in dissociation effects) rs79749176- SLC22A15Intra Response to  9.00E−06* 39.0894 unit [22.49-55.69] 30552317GCST007316 A/EUR genic ketamine in increase bipolar disorder or majordepression (increase in antidepressant effects)

TABLE 2A Part 2. Regulatory element associated with enhancer andsuperenhancer SNPs that have been found in the ketamine efficacysub-network. Validated casual GWAS SNP Promoters human disease Reported/(EPD new); enhancer- Superenhancers Population(s) PromID Enhancerspromoter pairs (dbSUPER) eQTL rs7623659- RHOA_1 Enhancer, T/EUR (chr3:astrocytes, bipolar 49411963- neuron, brain; 49412022) GH03J049377:chr3: 49414635- 49417768 (ENCODE (Z-Lab), Ensembl, dbSUPER; GeneHancerDoubleElite) rs12229654- CUX2_1 Enhancer, 1E0E−11; G/ASN (chr12: Bipolarneuron, Brain 111033920- H9 neuronal Frontal 111033979) cells, neuralCortex progenitor cells; chr12: 111473237- 111473713 (chr12: 111035433-111035909, eRNA Score: 49 (ENCODE, FANTOM, Ensembl) rs30266- Enhancer,7.5E−09, A/EUR Bipolar neuron, Testis H9 neuronal cells, prefrontalcortex, cingulate cortex; ENSR00000764485: chr5: 105386601- 105387599(Vista, GeneHancer Elite; Ensembl) rs61902811- TMPRSS5_1 Enhancer,1.80E−11; G/EUR (chr11: neural Brain 113706292- progenitor cell;Hippocampus 113706351) GH11J11370: chr11: 113705465- 113707068)rs12229654- CUX2_1 Enhancer, 1E0E−11; G/ASN (chr12: Bipolar neuron,Brain 111033920- H9 neuronal Frontal 111033979) cells, neural Cortexprogenitor cells; chr12: 111473237- 111473713 (chr12: 111035433-111035909, eRNA Score: 49 (ENCODE, FANTOM, Ensembl) rs6265- Enhancer,Brain DE_00099: 5.08E−06; T/EUR Hippocampus chr11: Nerve tibial Middle,Brain 27739147- Anterior 27742290; Caudate, Brain Depression CingulateGyrus (ENCODE) rs7227069- DCC_2 Enhancer, SE_08791 1.0E−21; A/EUR(chr18: prefrontal cortex, (Brain - Mid Brain 52340148- cingulatecortex; frontal lobe) Cingiulate 52340207) FANTOM: Gyrus chr18:52341963- 52342477, eRNA Score: 32, Cells: neural progenitor cellsrs12967143- TCF4_1 Enhancer, DE_00452: SE_06758 1E−11; G/EUR (chr18:prefrontal chr18: SE_06128 Brain 55401678- cortex, cingulate 55401678-(Brain - Frontal 55401737) cortex (Vista, 55401737; Hippocampus CortexTCF4_9 GeneHancer Depression middle); (chr18: Elite) SE_08823 55401970-(Brain - Mid 55402029) frontal lobe); TCF4_4 SE_04894 (chr18: (Brain -Cingulate 55403630- gyrus); 55403689) SE_07784 (Brain - Inferiortemporal lobe); SE_03220 (Brain - Angular gyrus); SE_04070 (Brain -Anterior caudate) rs4938021- TMPRSS5_1 Enhancer, 1.80E−23; T/EUR (chr11:neural Brain 113706292- progenitor ceil; Amygdala 113706351) GH11J11370:chr11: 113705465- 113707068) rs6589377- TMPRSS5_1 Enhancer, 1.80E−11;A/EUR (chr11: neural Brain 113706292- progenitor cell; Hippocampus113706351) GH11J11370: chr11: 113705465- 113707068) rs6589377- TMPRSS5_1Enhancer, 1.0E−21; A/EUR (chr11: neural Brain 113706292- progenitorcell; Cingiulate 113706351) GH11J11370: Gyrus chr11: 113705465-113707068) rs7932640- Enhancer, H1 1.0E−03; T/EUR progenitor Brainneurons; Cingulate ENSR00000438504 Gyrus (chr11: 89119401- 89120000rs7111031- TTC12_1 Enhancer, 1E−37; A/EUR (chr11: Bipolar neuron, Brain113314539- Brain; Anterior 113314598) ENSR00000045120 Caudate (chr11:113313800- 113315601) (ENCODE, FANTOM, Ensmbl) rs11662271- DCC_2Enhancer, 1.55E−51; T/EUR (chr18: prefrontal Brain 52340148- cortex,cingulate Frontal 52340207) cortex; Cortex FANTOM: chr18: 52341963-52342477, eRNA Score: 32, Cells: neural progenitor cells rs1373178-Enhancer, 1E−15; G/EUR hippocampus, Brain ERNA score 21 Amygdala(ENCODE, VISTA) rs1925950- MEF2D_2 Enhancer, SE_04141 1E−89; G/EUR(chr1: bipolar neurons, (Brain - Anterior Brain 156490647- braincaudate); Cingulate 156490706) (ENCODE, SE_08791 Gyrus MEF2D_1 Ensembl,(Brain - Mid (chr1: dbSUPER, frontal lobe); 156500765- GeneHancerSE_05851 156500824) Double Eite); SE_06826 FANTOM: (Brain - chr1:Hippocampus 156502901- middle); 156503160, SE_04925 eRNA Score: 319;(Brain - Cingulate Cells: Neural gyrus); progenitor cells SE_07824(Brain - Inferior temporal lobe); SE_03183 (Brain - Angular gyrus);SE_02565 (Astrocytes); rs8084280- DCC_2 Enhancer, 1.0E−09; T/EUR (chr18:neural Brain 52340148- progenitor cell Cingulate 52340207) (ENCODE)Cortex rs7111031- ANKK1_1 Enhancer, H1 1E−06; A/EUR (chr11: neuronalBrain 113387730- progenitor cells, Hippocampus 113387789) brain,astrocytes, bipolar neurons; GH11J113505: chr11: 113375857- 113378219;(ENCODE, Ensemble, Vista, MASTERMIND); FANTOM: chr11: 113375857-113376013 (chr11: 113505135- 113505291, eRNA Score: 2) rs1925950-MEF2D_2 Enhancer, SE_04141 1E−89; G/EUR (chr1: bipolar neurons, (Brain -Anterior Brain 156490647- brain caudate); Cingulate 156490706) (ENCODE,SE_08791 Gyrus MEF2D_1 Ensembl, (Brain - Mid (chr1: dbSUPER, frontallobe); 156500765- GeneHancer SE_05851 156500824) Double Eite); SE_06826FANTOM: (Brain - chr1: Hippocampus 156502901- middle); 156503160,SE_04925 eRNA (Brain - Cingulate Score: 319; gyrus); Cells: NeuralSE_07824 progenitor cells (Brain - Inferior temporal lobe); SE_03183(Brain - Angular gyrus); SE_02565 (Astrocytes); rs12520354- Enhancer,SE_33104 A/EUR Bipolar neurons, (Brain - Cingulate brain; gyrus)ENSR00000183204 (chr5: 80955800- 80956401) rs599550- TCF4_2 Enhancer,DE_00452: SE_06758 1E−52; A/EUR (chr18: neural chr18: SE_06128 Brain55588182- progenitor cells, 55401678- (Brain - Frontal 55588241) brain55401737; Hippocampus Cortex TCF4_3 (GeneHancer Depression middle);(chr18: Elite, Vista, SE_08823 55588605- MASTERMIND) (Brain - Mid55588664) frontal lobe); SE_04894 (Brain - Cingulate gyrus); SE_07784(Brain - Inferior temporal lobe); SE_03220 (Brain - Angular gyrus);SE_04070 (Brain - Anterior caudate), SE_33242 (H1-ESC) rs613872- TCF4_2Enhancer, DE_00452: SE_33242 1E−12; C/EUR (chr18: neural chr18: (H1-ESC)Brain 55588182- progenitor cells, 55401678- Cingulate 55588241) brain55401737; Gyrus TCF4_3 (GeneHancer Depression (chr18: Elite, Vista,55588605- MASTERMIND) 55588664) rs8084280- DCC_2 Enhancer, 1.0E−09;T/EUR (chr18: neural Brain 52340148- progenitor cell Cingulate 52340207)(ENCODE) Cortex rs11662271- DCC_2 Enhancer, 1.55E−51; T/EUR (chr18:prefrontal Brain 52340148- cortex, cingulate Frontal 52340207) cortex;Cortex FANTOM: chr18: 52341963- 52342477, eRNA Score: 32, Cells: neuralprogenitor cells rs12967143- TCF4_1 Enhancer, DE_00452: SE_06758 1E−11;C/EUR (chr18: prefrontal chr18: SE_06128 Brain 55401678- cortex,cingulate 55401678- (Brain - Frontal 55401737) cortex (Vista, 55401737;Hippocampus Cortex TCF4_9 GeneHancer Depression middle); (chr18: Elite)SE_08823 55401970- (Brain - Mid 55402029) frontal lobe); TCF4_4 SE_04894(chr18: (Brain - Cingulate 55403630- gyrus); 55403689) SE_07784 (Brain -Inferior temporal lobe); SE_03220 (Brain - Angular gyrus); SE_04070(Brain - Anterior caudate) rs8181326- SLIT1_4 Enhancer, Brain SE_33104A/EUR (chr10: Hippocampus (Brain - Cingulate 97185502- Middle, Braingyrus) 97185561) Anterior Caudate, Brain Cingulate Gyrus (ENCODE)rs72930774- TCF4_1 Enhancer, DE_00452: SE_06758 1E−11; A/EUR (chr18:prefrontal chr18: SE_06128 Brain 55401678- cortex, cingulate 55401678-(Brain - Frontal 55401737) cortex (Vista, 55401737; Hippocampus CortexTCF4_9 GeneHancer Depression middle); (chr18: Elite) SE_08823 55401970-(Brain - Mid 55402029) frontal lobe); TCF4_4 SE_04894 (chr18: (Brain -Cingulate 55403630- gyrus); 55403689) SE_07784 (Brain - Inferiortemporal lobe); SE_03220 (Brain - Angular gyrus); SE_04070 (Brain -Anterior caudate) rs2958162/ TCF4_2 Enhancer, DE_00452: SE_067581.36E−20; EUR (chr18: prefrontal chr18: SE_06128 Brain 55588182- cortex,cingulate 55401678- (Brain - Cingulate 55588241) cortex (Vista,55401737; Hippocampus Gyrus TCF4_3 GeneHancer Depression middle);(chr18: Elite) SE_08823 55588605- (Brain - Mid 55588664) frontal lobe);SE_04894 (Brain - Cingulate gyrus); SE_07784 (Brain - Inferior temporallobe); SE_03220 (Brain - Angular gyrus); SE_04070 (Brain - Anteriorcaudate) rs7949802- ANKK1_1 Enhancer, H1 1E−06; T/EUR (chr11: neuronalBrain 113387730- progenitor cells, Hippocampus 113387789) brain,astrocytes, bipolar neurons; GH11J113505; chr11: 113375857- 113378219;(ENCODE, Ensemble, Vista, MASTERMIND); FANTOM: chr11: 113375857-113376013 (chr11: 113505135- 113505291, eRNA Score: 2) rs1660237-Enhancer, DE_00452: T/EUR prefrontal chr18: cortex, cingulate 55401678-cortex (Vista, 55401737; GeneHancer Depression Elite) rs674437-Enhancer, H1 1E−27; M/EUR progenitor Brain neurons; AmygdalaENSR00000438504 (chr11: 89119401- 89120000 rs624244/ TCF4_2 Enhancer,DE_00452: SE_06352 1.36E−20; EUR (chr18: prefrontal chr18: SE_06762Brain 55588182- cortex, cingulate 55401678- (Brain - Cingulate 55588241)cortex (Vista, 55401737; Hippocampus Gyrus TCF4_3 GeneHancer Depressionmiddle) (chr18: Elite) SE_08840 55588605- (Brain - Mid 55588664) frontallobe) SE_04109 (Brain - Anterior caudate) SE_07745 (Brain - Inferiortemporal lobe); SE_04840 (Brain - Cingulate gyrus) SE_03237 (Brain -Angular gyrus) rs8099160/ DCC_2 Enhancer, 1E−12; EUR (chr18: prefrontalBrain 52340148- cortex, cingulate Cingulate 52340207) cortex; GyrusFANTOM: chr18: 52341963- 52342477, eRNA Score: 32, Cells: neuralprogenitor cells rs674437- Enhancer, H1 1E−27; A/EUR progenitor Brainneurons; Amygdala ENSR00000438504 (chr11: 89119401- 89120000 rs599550-TCF4_2 Enhancer, DE_00452: SE_06758 1E−52; A/EUR (chr18: neural chr18:SE_06128 Brain 55588182- progenitor cells, 55401678- (Brain - Cingulate55588241) brain 55401737; Hippocampus Gyrus TCF4_3 (GeneHancerDepression middle); (chr18: Elite, Vista, SE_08823 55588605- MASTERMIND)(Brain - Mid 55588664) frontal lobe); SE_04894 (Brain - Cingulategyrus); SE_07784 (Brain - Inferior temporal lobe); SE_03220 (Brain -Angular gyrus); SE_04070 (Brain - Anterior caudate), SE_33242 (H1-ESC)rs7117514- Enhancer, 1E−05; G/EUR prefrontal Brain cortex Frontal Cortexrs2163971- Enhancer, 1E−11; T/EUR Brain: Brain ENSR00000692682 Frontal(chr3: Cortex 84983601- 84985999, Type: Proximal) rs4936277- ANKK1_1Enhancer, H1 1E−06; A/EUR (chr11: neuronal Brain 113387730-progenitorcells, Hippocampus 113387789) brain, astrocytes, bipolarneurons; GH11J113505: chr11: 113375857- 113378219; (ENCODE, Ensemble,Vista, MASTERMIND); FANTOM: chr11: 113375857- 113376013 (chr11:113505135- 113505291, eRNA Score: 2) rs13357015/ RASG Enhancer, SE_331041E−10; EUR RF2_1 Bipolar Neuron, (Brain - Cingulate Brain (chr5: Brain,gyrus) Cingulate 80960623- Astrocytes; Gyrus 80960682) GH05J080966rs17601612- ANKK1_1 Enhancer, H1 1E−06; C/EUR (chr11: neuronal Nucleus113387730- progenitor cells, accumbens 113387789) brain, astrocytes,bipolar neurons; GH11J113505: chr11: 113375857- 113378219; (ENCODE,Ensemble, Vista, MASTERMIND); FANTOM: chr11: 113375857- 113376013(chr11: 113505135- 113505291, eRNA Score: 2) rs8084351/ DCC_2 Enhancer,SE_08791 1.0E−21; EUR (chr18: prefrontal cortex, (Brain - Mid Brain52340148- cortex; cingulate frontal lobe) Cingiulate 52340207) FANTOM:Gyrus chr18: 52341963- 52342477, eRNA Score: 32, Cells: neuralprogenitor rs61687445- TTC12_1 Enhancer, 1E−37; A/EUR (chr11: Bipolarneuron, Brain 113314539- Brain; Anterior 113314598) ENSR00000045120Caudate (chr11: 113313800- 113315601) (ENCODE, FANTOM, Ensmbl)rs35738585/ TTC12_1 Enhancer, Bipolar 1E−37; EUR (chr11: neuron, Brain;Brain 113314539- ENSR00000045120 Anterior 113314598) (chr11: Caudate113313800- 113315601) (ENCODE, FANTOM, Ensmbl) rs9636107- TCF4_2Enhancer, DE_00452: SE_06758 1E−20; G/EUR (chr18: neural chr18: SE_06128Brain 55588182- progenitor cells, 55401678- (Brain - Cingulate 55588241)brain 55401737; Hippocampus Gyrus TCF4_3 (GeneHancer Depression middle);(chr18: Elite, Vista, SE_08823 55588605- MASTERMIND) (Brain - Mid55588664) frontal lobe); SE_04894 (Brain - Cingulate gyrus); SE_07784(Brain - Inferior temporal lobe); SE_03220 (Brain - Angular gyrus);SE_04070 (Brain - Anterior caudate), SE_33242 (H1-ESC) rs12968428- DCC_2Enhancer, SE_08791 1.0E−21; A/EUR (chr18: prefrontal cortex, (Brain -Mid Brain 52340148- cingulate cortex; frontal lobe) Cingiulate 52340207)FANTOM: Gyrus chr18: 52341963- 52342477, eRNA Score: 32, Cells: neuralprogenitor cells rs8138473/ Enhancer, SE_08247 1E−20; EUR neural(Brain - Inferior Brain progenitor cells, temporal lobe) Amygdala brainSE_53463 (GeneHancer (Spleen) Elite, Vista, SE_03550 MASTERMIND)(Brain - Angular gyrus) SE_06357 (Brain - Hippocampus middle) SE_04497(Brain - Anterior caudate) SE_09211 (Brain - Amygdala) rs619466- TCF4_2Enhancer, DE_00452: SE_06758 1E−20; G/EUR (chr18: neural chr18: SE_06128Brain 55588182- progenitor cells, 55401678- (Brain - Cingulate 55588241)brain 55401737; Hippocampus Gyrus TCF4_3 (GeneHancer Depression middle);(chr18: Elite, Vista, SE_08823 55588605- MASTERMIND) (Brain - Mid55588664) frontal lobe); SE_04894 (Brain - Cingulate gyrus); SE_07784(Brain - Inferior temporal lobe); SE_03220 (Brain - Angular gyrus);SE_04070 (Brain - Anterior caudate), SE_33242 (H1-ESC) rs1431181- DCC_2Enhancer, 1.0E−09; A/EUR (chr18: neural Brain 52340148- progenitor cellCingulate 52340207)/ (ENCODE) Gyrus rs1261070- TCF4_1 Enhancer,DE_00452: SE_06758 1E−11; ?/EUR (chr18: prefrontal chr18: SE_06128 Brain55401678- cortex, cingulate 55401678- (Brain - Frontal 55401737) cortex(Vista, 55401737; Hippocampus Cortex TCF4_9 GeneHancer Depressionmiddle); (chr18: Elite) SE_08823 55401970- (Brain - Mid 55402029)frontal lobe); TCF4_4 SE_04894 (chr18: (Brain - Cingulate 55403630-gyrus); 55403689) SE_07784 (Brain - Inferior temporal lobe); SE_03220(Brain - Angular gyrus); SE_04070 (Brain - Anterior caudate) rs611439-TCF4_1 Enhancer, DE_00452: SE_06758 1E−11; ?/EUR (chr18: prefrontalchr18: SE_06128 Brain 55401678- cortex, cingulate 55401678- (Brain -Frontal 55401737) cortex (Vista, 55401737; Hippocampus Cortex TCF4_9GeneHancer Depression middle); (chr18: Elite) SE_08823 55401970-(Brain - Mid 55402029) frontal lobe); TCF4_4 SE_04894 (chr18: (Brain -Cingulate 55403630- gyrus); 55403689) SE_07784 (Brain - Inferiortemporal lobe); SE_03220 (Brain - Angular gyrus); SE_04070 (Brain -Anterior caudate) rs7231748- TCF4_2 Enhancer, DE_00452: SE_06758 1E−20;A/EUR (chr18: neural chr18: SE_06128 Brain 55588182- progenitor cells,55401678- (Brain - Cingulate 55588241) brain 55401737; Hippocampus GyrusTCF4_3 (GeneHancer Depression middle); (chr18: Elite, Vista, SE_0882355588605- MASTERMIND) (Brain - Mid 55588664) frontal lobe); SE_04894(Brain - Cingulate gyrus); SE_07784 (Brain - Inferior temporal lobe);SE_03220 (Brain - Angular gyrus); SE_04070 (Brain - Anterior caudate),SE_33242 (H1-ESC) rs4277413- DCC_2 Enhancer, 1.0E−09; A/EUR (chr18:neural Brain 52340148- progenitor cells, Cingulate 52340207) (ENCODE)Gyrus rs1050316/ MEF2D_2 Enhancer, SE_04141 1E−89; EUR (chr1: bipolarneurons, (Brain - Anterior Brain 156490647- brain caudate); Cingulate156490706) (ENCODE, SE_08791 Gyrus MEF2D_1 Ensembl, (Brain - Mid (chr1:dbSUPER, frontal lobe); 156500765- GeneHancer SE_05851 156500824) DoubleEite); SE_06826 FANTOM: (Brain - chr1: Hippocampus 156502901- middle);156503160, SE_04925 eRNA Score: 319; (Brain - Cingulate Cells: Neuralgyrus); progenitor cells SE_07824 (Brain - Inferior temporal lobe);SE_03183 (Brain - Angular gyrus); SE_02565 (Astrocytes); rs139438618/SEMA3A_2 Enhacner, — — 1E−10; AFR (chr7: Bipolar neurons, Brain84194779- Brain, neural occiptal 84194838) progenitor cells; CortexSEMA3A_1 ENSR00000214578 (chr7: (chr7: 84194983- 84190600- 84195042)84196801) rs8089865- DCC_2 Enhancer, 1.0E−09; A/EUR (chr18: neural Brain52340148- progenitor cell Cingulate 52340207) (ENCODE) Gyrus rs12958048-TCF4_2 Enhancer, DE_00452: SE_06758 1E−20; A/EUR (chr18: neural chr18:SE_06128 Brain 55588182- progenitor cells, 55401678- (Brain - Cingulate55588241) brain 55401737; Hippocampus Gyrus TCF4_3 (GeneHancerDepression middle); (chr18: Elite, Vista, SE_08823 55588605- MASTERMIND)(Brain - Mid 55588664) frontal lobe); SE_04894 (Brain - Cingulategyrus); SE_07784 (Brain - Inferior temporal lobe) SE_33496 (H2171);SE_03220 (Brain - Angular gyrus); SE_04070 (Brain - Anterior caudate)rs9636107/ TCF4_1 Enhancer, DE_00452: SE_06758 1E−11; EUR (chr18:prefrontal chr18: SE_06128 Brain 55401678- cortex, cingulate 55401678-(Brain - Frontal 55401737) cortex (Vista, 55401737; Hippocampus CortexTCF4_9 GeneHancer Depression middle); (chr18: Elite) SE_08823 55401970-(Brain - Mid 55402029) frontal lobe); TCF4_4 SE_04894 (chr18: (Brain -Cingulate 55403630- gyrus); 55403689) SE_07784 (Brain - Inferiortemporal lobe); SE_03220 (Brain - Angular gyrus); SE_04070 (Brain -Anterior caudate) rs17598729- TCF4_1 Enhancer, DE_00452: SE_06758 1E−11;C/ASN (chr18: prefrontal chr18: SE_06128 Brain 55401678- cortex,cingulate 55401678- (Brain - Frontal 55401737) cortex (Vista, 55401737;Hippocampus Cortex TCF4_9 GeneHancer Depression middle); (chr18: Elite)SE_08823 55401970- (Brain - Mid 55402029) frontal lobe); TCF4_4 SE_04894(chr18: (Brain - Cingulate 55403630- gyrus); 55403689) SE_07784 (Brain -Inferior temporal lobe); SE_03220 (Brain - Angular gyrus); SE_04070(Brain - Anterior caudate) rs4801157/ TCF4_2 Enhancer, DE_00452:SE_06758 1E−20; EUR (chr18: neural chr18: SE_06128 Brain 55588182-progenitor cells, 55401678- (Brain - Cingulate 55588241) brain 55401737;Hippocampus Gyrus TCF4_3 (GeneHancer Depression middle); (chr18: Elite,Vista, SE_08823 55588605- MASTERMIND) (Brain - Mid 55588664) frontallobe); SE_04894 (Brain - Cingulate gyrus); SE_07784 (Brain - Inferiortemporal lobe) SE_33496 (H2171); SE_03220 (Brain - Angular gyrus);SE_04070 (Brain - Anterior caudate) rs4384683- DCC_2 Enhancer, 1.0E−09;G/EUR (chr18: neural Brain 52340148- progenitor cell Cingulate 52340207)(ENCODE) Gyrus rs674437- — Enhancer, H1 1E−27; A/EUR progenitor Brainneurons; Amygdala ENSR00000438504 (chr11: 89119401- 89120000 rs4938021-TMPRSS5_1 Enhancer, 1.0E−21; T/EUR (chr11: neural Brain 113706292-progenitor cell; Cingiulate 113706351) GH11J11370: Gyrus chr11:113705465- 113707068) rs624244- TCF4_1 Enhancer, DE_00452: SE_067581E−11; A/EUR (chr18: prefrontal chr18: SE_06128 Brain 55401678- cortex,cingulate 55401678- (Brain - Frontal 55401737) cortex (Vista, 55401737;Hippocampus Cortex TCF4_9 GeneHancer Depression middle); (chr18: Elite)SE_08823 55401970- (Brain - Mid 55402029) frontal lobe); TCF4_4 SE_04894(chr18: (Brain - Cingulate 55403630- gyrus); 55403689) SE_07784 (Brain -Inferior temporal lobe); SE_03220 (Brain - Angular gyrus); SE_04070(Brain - Anterior caudate) rs674437- Enhancer, H1 1E−27; M/EURprogenitor Brain neurons; Amygdala ENSR00000438504 (chr11: 89119401-89120000 rs7228159- TCF4_1 Enhancer, DE_00452: SE_06758 1E−11; A/EUR(chr18: prefrontal chr18: SE_06128 Brain 55401678- cortex, cingulate55401678- (Brain - Frontal 55401737) cortex (Vista, 55401737;Hippocampus Cortex TCF4_9 GeneHancer Depression middle); (chr18: Elite)SE_08823 55401970- (Brain - Mid 55402029) frontal lobe); TCF4_4 SE_04894(chr18: (Brain - Cingulate 55403630- gyrus); 55403689) SE_07784 (Brain -Inferior temporal lobe); SE_03220 (Brain - Angular gyrus); SE_04070(Brain - Anterior caudate) rs61905363- ANKK1_1 Enhancer, H1 1E−06; T/EUR(chr11: neuronal Brain 113387730- progenitor cells, Hippocampus113387789) brain, astrocytes, bipolar neurons; GH11J113505: chr11:113375857- 113378219; (ENCODE, Ensemble, Vista, MASTERMIND); FANTOM:chr11: 113375857- 113376013 (chr11: 113505135- 113505291, eRNA Score: 2)rs4936277- ANKK1_1 Enhancer, H1 1E−06; G/EUR (chr11: neuronal Brain113387730- progenitor cells, Hippocampus 113387789) brain, astrocytes,bipolar neurons; GH11J113505: chr11: 113375857- 113378219; (ENCODE,Ensemble, Vista, MASTERMIND); FANTOM: chr11: 113375857- 113376013(chr11: 113505135- 113505291, eRNA Score: 2) rs17041417- Enhancer,1E−16; A/EUR neural stem Brain progenitor cell Amygdala rs17041417-Enhancer, 1E−16; A/EUR neural stem Brain progenitor cell Amygdalars17041417- Enhancer, 1E−16; A/EUR neural stem Brain progenitor cellAmygdala rs9811546- Enhancer, 1E11; A/EUR Brain: Brain ENSR00000692682Cingulate (chr3: Gyrus 84983601- 84985999, Type: Proximal) rs17041417-Enhancer, 1E−16; A/EUR Brain: Brain ENSR00000692682 Amygdala (chr3:84983601- 84985999, Type: Proximal) rs12575685/ Enhancer, brain SE_69042EUR (chr11: (H9 neurons) 70367951- 70368077) FANTOM, Ensembl, VISTArs2274316- MEF2D_2 Enhancer, SE_04141 1E−89; C/EUR (chr1: bipolarneurons, (Brain - Anterior Brain 156490647- brain caudate); Cingulate156490706) (ENCODE, SE_08791 Gyrus MEF2D_1 Ensembl, (Brain - Mid (chr1:dbSUPER, frontal lobe); 156500765- GeneHancer SE_05851 156500824) DoubleEite); SE_06826 FANTOM: (Brain - chr1: Hippocampus 156502901- middle);156503160, SE_04925 eRNA Score: (Brain - Cingulate 319; Cells: gyrus);Neural SE_07824 progenitor cells (Brain - Inferior temporal lobe);SE_03183 (Brain - Angular gyrus); SE_02565 (Astrocytes); rs775766-Enhancer, SE_33104 A/EUR neural stem (Brain - Cingulate progenitor cell,gyrus) embryonic human cerebral cortex rs310763- PPARG_2 Enhancer, C/EUR(chr3: bipolar neurons, 12287875- brain 12287934) (ENCODE, PPARG_3Ensembl, (chr3: dbSUPER, 12288326- GeneHancer 12288385) Double Eite);PPARG_1 FANTOM: (chr3: chr1: 12289021- 156502901- 12289080) 156503160,eRNA Score: 319; Cells: Neural progenitor cells rs310763- PPARG_2Enhancer, C/EUR (chr3: bipolar neurons, 12287875- brain 12287934)(ENCODE, PPARG_3 Ensembl, (chr3: dbSUPER, 12288326- GeneHancer 12288385)Double Eite); PPARG_1 FANTOM: (chr3: chr1: 12289021- 156502901-12289080) 156503160, eRNA Score: 319; Cells: Neural progenitor cellsrs310763- PPARG_2 Enhancer, C/EUR (chr3: bipolar neurons, 12287875-brain 12287934) (ENCODE, PPARG_3 Ensembl, (chr3: dbSUPER, 12288326-GeneHancer 12288385) Double Eite); PPARG_1 FANTOM: (chr3: chr1:12289021- 156502901- 12289080) 156503160, eRNA Score: 319; Cells: Neuralprogenitor cells rs1016306- — Enhancer, — — T/EUR Brain: ENSR00000692682(chr3: 84983601- 84985999, Type: Proximal) rs935526- Enhancer, SE_33104T/EUR fronatl cortex, (Brain - Cingulate cingulate cortex, gyrus)neurons; ENSR00000691539 (chr3: 75936001- 75936200, rs161645- A/EURrs17211233- RASGRF21 Enhancer, SE_33104 T/EUR (chr5: Bipolar Neuron,(Brain - Cingulate 80960623- Brain, gyrus) 80960682) Astrocytes;GH05J080966 rs11214606/ TMPRSS5_1 Enhancer, 1.0E−21; EUR (chr11: neuralBrain 113706292- progenitor cell; Anterior 113706351) GH11J11370:Caudate chr11: 113705465- 113707068) rs1400237/ Enhancer, SE_33104 EURfronatl cortex, (Brain - Cingulate cingulate cortex, gyrus) neurons;ENSR00000691539 (chr3: 75936001- 75936200, rs1846786- T/EUR rs4855976/Enhancer, SE_33104 EUR fronatl cortex, (Brain - Cingulate cingulatecortex, gyrus) neurons; ENSR00000691539 (chr3: 75936001- 75936200,rs79749176- SLC2 Enhancer, SE_06669 1E−08; A/EUR 2A15_1 frontal SE_06902Brain (chr1: cortex (chr1: (Brain - Anterior 115976484- 116520279-Hippocampus Caudate 115976543) 116520650) middle) SE_05076 (Brain -Cingulate gyrus) SE_09933 (CD14+ monocytes) SE_03600 (Brain - Angulargyrus) SE_04498 (Brain - Anterior caudate) SE_08098 (Brain - Inferiortemporal lobe)

TABLE 2A Part 3. Chromatin interactions for enhancer and superenhancerSNPs that have been found in the ketamine efficacy sub-network.. Judgedby machine learning to be causal Enhancer in neural GWAS SNP RNA co-cell lines Reported/ HGREEN expression but not Population(s) Hi-C Scorescore p-value HepG2? rs7623659-T/ 0.0001: Cingulate cortex 1.00E−122.00E−06 Yes EUR rs12229654-G/ 0.0001: Frontal cortex 1.00E−18 2.20E−09Yes ASN rs30266-A/ 0.01: Frontal cortex Yes EUR rs61902811-G/ 0.0001:Hipoocampus 1.00E−07 Yes EUR rs12229654-G/ 0.001: Frontal cortex1.00E−18 2.20E−23 Yes ASN rs6265-T/ 1.10E−10 Yes EUR rs7227069-A/0.0000000001: Cingulate cortex 1.50E−20 1.00E−05 Yes EUR rs12967143-G/0.0000000001: Frontal cortex 1.00E−05 1.10E−10 Yes EUR rs4938021-T/0.0000000001: Amygdala Yes EUR rs6589377-A/ 0.0001: Hippocampus 1.00E−07EUR rs6589377-A/ 0.00000000011: Cingulate cortex 1.00E−10 Yes EURrs7932640-T/ 0.0001: Cingulate cortex 1.50E−10 Yes EUR rs7111031-A/0.00000000011: Anterior caudate Yes EUR rs11662271-T/ 0.00000000011:Frontal cortex 1.00E−07 Yes EUR rs1373178-G/ 0.0001: Amygdala 1.50E−10Yes EUR rs1925950-G/ 0.0000000001: Cingulate cortex 6.10E−11 Yes EURrs8084280-T/ 0.0000000001: Cingulate cortex 6.30E−10 No EUR rs7111031-A/0.0000001: Hippocampus 3.30E−06 Yes EUR rs1925950-G/0.00000000000000000000000000000000011: 6.10E−11 Yes EUR Cingulate cortexrs12520354-A/ 0.0000000001: Cingulate cortex Yes EUR rs599550-A/0.00000000011: Frontal cortex 2.00E−05 Yes EUR rs613872-C/ 0.0001:Cingulate cortex Yes EUR rs8084280-T/ 0.00000000011: Cingulate cortex6.30E−10 Yes EUR rs11662271-T/ 0.0000000001: Frontal cortex 1.00E−07 YesEUR rs12967143-C/ 0.0000000001: Frontal cortex 1.00E−05 1.10E−10 Yes EURrs8181326-A/ 0.00000001: Cingulate cortex Yes EUR rs72930774-A/0.0000000001: Frontal cortex 1.00E−05 1.10E−10 Yes EUR rs2958162/0.0000000001: Cingulate cortex Yes EUR rs7949802-T/ 0.0000000001:Hippocampus 3.30E−06 Yes EUR rs1660237-T/ 0.00001: Frontal cortex YesEUR rs674437-A/ 0.0000000001: Amygdala 1.80E−09 Yes EUR rs624244/0.0000000001: Cingulate cortex Yes EUR rs8099160/ 0.0000000001:Cingulate cortex 1.00E−07 1.00E−21 Yes EUR rs674437-A/ 0.0000000001:Amygdala 1.80E−09 Yes EUR rs599550-A/ 0.0000000001: Cingulate cortex2.00E−05 EUR rs7117514-G/ 0.001: Frontal cortex Yes EUR rs2163971-T/0.00000001: Frontal cortex EUR rs4936277-A/ 0.000001: Nucleus Accumbens4.00E−05 EUR rs13357015/ 0.0001: Cingulate cortex Yes EUR rs17601612-C/0.000001: Nucleus Accumbens 4.00E−05 EUR rs8084351/ 0.0000000001:Cingulate cortex 1.50E−20 1.00E−05 Yes EUR rs61687445-A/ 0.0000000001:Anterior caudate Yes EUR rs35738585/ 0.0000000001: Anterior caudate EURrs9636107-G/ 0.0000000001: Cingulate cortex 2.00E−05 EUR rs12968428-A/0.0000000001: Cingulate cortex 1.50E−20 1.00E−05 EUR rs8138473/0.000000001: Amygdala 1.00E−04 Yes EUR rs619466-G/ 0.0000000001:Cingulate cortex 2.00E−05 Yes EUR rs1431181-A/ 0.0000000001: Cingulatecortex 6.30E−10 Yes EUR rs1261070-?/ 0.000000001: Frontal cortex1.00E−05 1.10E−10 Yes EUR rs611439-?/ 0.00001: Frontal cortex 1.00E−051.10E−10 Yes EUR rs7231748-A/ 0.0000000001: Cingulate cortex 2.00E−051.00E−11 Yes EUR rs4277413-A/ 0.0000000001: Cingulate cortex 6.30E−10Yes EUR rs1050316/ 0.000001: Cingulate cortex 6.10E−11 Yes EURrs139438618/ 0.0000000001: Nucleus Accumbens — — Yes AFR rs8089865-A/0.00000001: Cingulate cortex 6.30E−10 Yes EUR rs12958048-A/0.0000000001: Cingulate cortex 2.00E−05 1.00E−11 Yes EUR rs9636107/0.0000000001: Frontal cortex 1.00E−05 1.10E−10 Yes EUR rs17598729-C/0.0000000001: Frontal cortex 1.00E−05 1.10E−10 Yes ASN rs4801157/0.0000000001: Cingulate cortex 2.00E−05 1.00E−11 Yes EUR rs4384683-G/0.0000000001: Cingulate cortex 6.30E−10 Yes EUR rs674437-A/0.0000000001: Amygdala 1.80E−09 — EUR rs4938021-T/ 0.0000000001:Cingulate cortex 1.00E−10 — Yes EUR rs624244-A/ 0.0000000001: Frontalcortex 1.00E−05 1.10E−10 Yes EUR rs674437-A/ 0.0000000001: Amygdala1.80E−09 EUR rs7228159-A/ 0.0000000001: Frontal cortex 1.00E−05 1.10E−10Yes EUR rs61905363-T/ 0.0000000001: Hippocampus 4.00E−05 Yes EURrs4936277-G/ 0.0000000001: Hippocampus 8.00E−08 Yes EUR rs17041417-A/0.0000000001: Amygdala 5.00E−11 Yes EUR rs17041417-A/ 0.0000000001:Amygdala 5.00E−11 Yes EUR rs17041417-A/ 0.0000000001: Amygdala 5.00E−11EUR rs9811546-A/ 0.0000000001: Cingulate cortex 6.10E−11 Yes EURrs17041417-A/ 0.0000000001: Amygdala 1.00E−11 EUR rs12575685/0.000000001: Fetal brain, female 5.00E−07 Yes EUR rs2274316-C/0.0000000001: Cingulate cortex 6.10E−11 Yes EUR rs775766-A/0.00000000000001: Cingulate cortex 1.00E−10 Yes EUR rs310763-C/0.0000000001: Frontal cortex Yes EUR rs310763-C/ 0.0000000001: Frontalcortex EUR rs310763-C/ 0.0000000001: Frontal cortex EUR rs1016306-T/0.0000000001: Amygdala — — Yes EUR rs935526-T/ 0.01: Cingulate cortex1.00E−10 Yes EUR rs161645-A/ Yes EUR rs17211233-T/0.00000000000000000000000001: 1.00E−10 2.00E−05 Yes EUR Cingulate cortexrs11214606/ 0.0000000001: Anterior caudate 5.00E−18 Yes EUR rs1400237/0.000000001: Cingulate cortex 1.00E−10 Yes EUR rs1846786-T/ Yes EURrs4855976/ 0.000000001: Cingulate cortex 1.00E−10 Yes EUR rs79749176-A/0.0000000001: Anterior caudate 1.00E−10 Yes EUR

TABLE 2B Part 1. Enhancer and superenhancer SNPs that have been found inthe ketamine adverse event sub-network. EBI- GWAS SNP Vari- OddsConfidence NHGRI Reported/ Reported ant Reported P- ratio IntervalPubMed Accension Population(s) gene(s) type trait value or beta (95%) IDNumber rs1051730- CHRNA5, Inter Smoking 6.00E−121 0.1 unit [0.091-0.111]30617275 GCST007602 A/ASN, CHRNA3 genic status increase EUR rs1051730-CHRNA5, Inter Smoking 3.00E−73 1.02 CPD [0.91-1.13] 20418890 GCST000666G/EUR CHRNA3 genic status decrease rs1051730- CHRNA5, Inter Smoking7.00E−69 0.8 CPD [0.70-0.90] 20418888 GCST000667 A/EUR CHRNA3 genicstatus increase rs1051730- CHRNA5, Inter Smoking 2.00E−66 0.08[0.07-0.09] 20418889 GCST000668 G/EUR CHRNA3 genic status unit decreasers2155646- NCAM1 Intra Smoking 3.00E−61 0.0181277 unit [0.016-0.02] 30643251 GCST007468 C/EUR genic status increase rs7938812- NCAM1 IntraSmoking 7.00E−48 0.029930087 unit [0.026-0.034] 30643258 GCST007474T/EUR genic status decrease rs3130820/ TRIM28 Inter Schizophrenia,2.00E−44 1.281 [1.25-1.32] 29483656 GCST006803 EUR genic chronic (F20)rs3918226- NOS3 Intra Medication 2.00E−37 0.145321 unit [0.12-0.17]31015401 GCST007930 T/EUR genic use (agents increase acting on therenin- angiotensin system) rs3918226/ NOS3 Intra Diastolic 4.00E−3329455858 GCS006187 EA (Han genic blood Chinese) pressure and smokingstatus rs3918226- NOS3 Intra Diastolic 4.00E−31 29912962 GCST006166T/AFR, genic blood AMR, ASN, pressure × EUR alcohol consumptioninteraction (2df test) rs3918226- NOS3 Intra Medication 7.00E−290.1665414 unit [0.14-0.2]  31015401 GCST007928 T/EUR genic use increase(diuretics) rs3918226- NOS3 Intra Medication 4.00E−26 0.16126491 unit[0.13-0.19] 31015401 GCST007929 T/EUR genic use (calcium increasechannel blockers) rs2155290/ NCAM1 Intra Risk-taking 4.00E−240.026843483 unit [0.022-0.032] 30643258 GCST007323 EUR genic behaviordecrease rs182812355- TRIM27, Inter Schizophrenia, 7.00E−23 1.206[1.17-1.24  30285260 GCST007201 A/ASN TRIM28 genic chronic (F20)rs3918226- NOS3 Intra Medication 8.00E−21 0.14636576 unit [0.12-0.18]31015401 GCST007927 T/EUR genic use (beta increase blocking agents)rs12764899/ BORCS7- Inter Cognitive 3.00E−20 0.02186565 unit[0.017-0.027] 31374203 GCST008595 EUR ASMT, genic ability, yearsincrease AS3MT of educational attainment or schizophrenia (pleiotropy)rs11191424- BORCS7- Inter Schizophrenia, 4.00E−20 1.0905125 [1.07-1.11]28991256 GCST004946 G/EUR ASMT, genic chronic AS3MT (F20) rs2007044/CACNA1C Intra Schizophrenia, 6.00E−20 1.0928961 1.07-1.11 29483656GCST006803 ASN genic chronic (F20) rs2159100/ CACNA1C IntraSchizophrenia, 3.00E−19 1.102 1.08-1.12 28991256 GCST004946 ASN, EURgenic chronic (F20) rs514465-A/ GABRA2 Intra Risk-taking 2.00E−18 0.0104unit  0.008-0.0128 30643258 GCST007323 EUR genic behavior increasers2007044- CACNA1C Intra Schizophrenia, 3.00E−18 1.0964912 [1.07-1.12]25056061 GCST002539 G/ASN, genic chronic EUR (F20) rs1024582- CACNA1CIntra Schizophrenia, 9.00E−18 1.1037527 1.08-1.13 30285260 GCST007201A/ASN genic chronic (F20) rs2007044/ CACNA1C Intra Schizophrenia,1.00E−17 1.098901 26198764 GCST002539 EUR genic chronic (F20)rs10774909/ NOS1 Intra Neuroticism, 5.00E−16 0.009250518 unit 0.007-0.0115 30643256 GCST007339 EUR genic general increase rs2007044/CACNA1C Intra Cognitive 2.00E−15 0.016178014 unit [0.012-0.02]  31374203GCST008595 EUR genic ability, years increase of educational attainmentor schizophrenia (pleiotropy) rs7192140 GRIN2A Intra Smoking 2.00E−150.00854911 unit [0.0064-0.0107] 30643251 GCST007468 genic statusdecrease rs2239030- CACNA1C Intra Risk-taking 6.00E−15 0.0093 unit0.0069-0.0117 30643258 GCST007323 A/EUR genic behavior increasers3918226- NOS3 Intra Systolic 2.00E−14 29912962 GCST006434 T/EA (Hangenic blood Chinese) pressure × alcohol consumption interaction (2dftest) rs11055991- ATF7IP Intra Cognitive 5.00E−14 0.0175 unit[0.013-0.022] 30038396 GCST006570 A/EUR genic performance decrease(MTAG) rs2007044/ CACNA1C Intra Autism 6.00E−14 1.0752687 1.05-1.1 28540026 GCST004521 EUR genic spectrum disorder or schizophreniars1024582- CACNA1C Intra Autism 8.00E−14 1.093 28540026 GCST004521 A/EURgenic spectrum disorder or schizophrenia rs7893279- CACNB2 IntraSchizophrenia, 9.00E−14 1.122 1.09-1.15 30285260 GCST007201 T/ASN, genicchronic EUR (F20) rs8042374- CHRNA3 Intra Schizophrenia, 2.00E−13 1.093[1.067-1.119] 28991256 GCST004946 A/EA (Han genic chronic Chinese) (F20)rs7893279- CACNB2 Intra Schizophrenia, 3.00E−13 1.117 [1.09-1.15]28991256 GCST004946 T/ASN, genic chronic EUR (F20) rs7893279- CACNB2Intra Schizophrenia, 5.00E−13 1.118 [1.09-1.15] 28991256 GCST004946 T,ASN, genic chronic EUR (F20) rs1006737/ CACNA1C Intra Schizophrenia6.00E−13 24280982 GCST002295 EUR genic or bipolar I disorder rs28681284-CHRNA5, Inter Schizophrenia, 6.00E−13 1.121 1.09-1.15 30285260GCST007201 C/ASN CHRNA3 genic chronic (F20) rs7893279- CACNB2 IntraSchizophrenia, 9.00E−13 1.117 1.09-1.15 28991256 GCST004946 T/ASN, genicchronic EUR (F20) rs3918226/ NOS3 Intra Systolic 2.00E−12 29455858GCST006188 EUR genic blood pressure (cigarette smoking interaction)rs7893279- CACNB2 Intra Schizophrenia, 2.00E−12 1.125 [1.088-1.162]25056061 GCST002539 T/ASN genic chronic (Han (F20) Chinese), EURrs1006737- CACNA1C Intra Schizophrenia, 5.00E−12 1.103 [1.08-1.13]23974872 GCST002149 A/EUR genic chronic (F20) rs111294930- LINC01470,Inter Schizophrenia, 9.00E−12 1.086 [1.06-1.11] 29483656 GCST006803A/EUR GRIA1 genic chronic (F20) rs7688285- GLRB Intro Panic 7.00E−121.50163 unit 1.27-1.67 28167838 C/AFR, nic disorder, increase EURstartle response, fear rs641574-A/ GRIA4 Intra Cognitive 1.00E−11 0.016unit 0.011-0.021 30038396 GCST006570 EUR genic function decreasers2298527- NCAM1 Intra Increased 3.00E−11 6.653 z 29255261 GCST005232C/EUR genic neuroticism score decrease rs7192140- GRIN2A Intra Smoking3.00E−11 0.016883379 unit [0.012-0.022] 30643251 GCST007468 C/EUR genicstatus decrease rs9292918- HCN1 Intra Schizophrenia, 4.00E−11 1.0729614unit 1.05-1.09 28991256 GCST004946 G/ASN genic chronic increase (F20)rs2155290- NCAM1 Intra Risky sexual 5.00E−11 0.015957858 unit[0.011-0.021] 30643258 GCST007326 C/EUR genic behavior decreasemeasurement rs16902086/ HCN1 Intra Schizophrenia, 6.00E−11 1.0683761unit 1.05-1.09 29283656 GCST006803 EUR genic chronic increase (F20)rs7893279- CACNB2 Intra Schizophrenia, 6.00E−11 1.12 26198764 GCST003048T/ASN genic chronic (Han (F20) Chinese), EUR rs2239030- CACNA1C IntraRisk-taking 7.00E−11 0.012764932 unit 0.0089-0.0166 30643258 GCST007323A/EUR genic behavior increase rs1837016- NQO1 Intra Risk-taking 7.00E−110.017263984 unit [0.012-0.022] 30643258 GCST007323 T/EUR genic tendency(4- decrease domain principal component model) rs111294930- LINC01470,Inter Schizophrenia, 1.00E−10 1.094 [1.064-1.124] 25056061 GCST002539A/ASN, GRIA1 genic chronic EUR (F20) rs62367520/ HCN1 Intra Smoking1.00E−10 0.186 29283656 GCST007468 EUR genic status rs11647445- GRIN2AIntra Bipolar I 1.00E−10 1.0785276 31043756 GCST008103 G/EUR genicdisorder rs7893279- CACNB2 Intra Schizophrenia, 1.00E−10 1.119[1.08-1.15] 30285260 GCST007201 T/ASN genic chronic (Han (F20) Chinese),EUR rs4782271- GRIN2A Intra Risk-taking 2.00E−10 0.01 unit[0.0069-0.0131] 30643258 GCST007325 A/EUR genic behavior increasers7893279- CACNB2 Intra Schizophrenia, 3.00E−10 1.1 [1.07-1.14] 28540026GCST004521 T/EUR genic autism spectrum disorder rs73047488/ GRIN2B IntraRisk-taking 3.00E−10 0.0078 unit 0.0054-0.0102 30643258 GCST007325 EURgenic behavior increase rs3825845- CHRNA3 Intra Schizophrenia, 4.00E−101.12 30285260 GCST007201 C/ASN genic chronic (F20) rs10744560/ CACNA1C,Intra Bipolar I, II 4.00E−10 1.07595 [1.05-1.1]  31043756 GCST008103 EURCACNA1C- genic disorder IT3 rs113551349/ SLC6A9 Intra ADHD and 5.00E−101.098901 [1.08-1.14] 30610198 GCST006983 EUR genic lifetime cannabis users2973155- GRIA1, Inter Schizophrenia, 8.00E−10 1.0638298 26198764GCST003048 C/EUR LINC01470 genic chronic (F20) rs13233131/ CHRM2 IntraRisk-taking 8.00E−10 0.0095 unit [0.0066-0.0124] 30643258 GCST007325 EURgenic behavior decrease rs758117-C/ CACNA1C Intra Schizophrenia,8.00E−10 1.0695187 [1.05-1.09] 30285260 GCST007201 ASN genic chronic(F20) rs872123/ GAD1 Intra Cognitive 1.00E−09 0.0208 unit [0.014-0.027]30038396 GCST006572 EUR genic function increase rs2973155- GRIA1 IntraSchizophrenia, 1.00E−09 1.0638298 26198764 GCST003048 C/EUR genicchronic (F20) rs13176930- LINC01470, Inter Schizophrenia, 1.00E−091.0718113 1.05-1.09 26198764 GCST007201 T/ASN GRIA1 genic chronic (F20)rs9292918/ HCN1 Intra Schizophrenia, 1.00E−09 1.0683761 [1.05-1.09]29283656 GCST007201 EUR genic chronic (F20) rs1478364- GRIA1, IntraRisk-taking 2.00E−09 0.0091 unit [0.0062-0.012]  30643258 GCST007325T/EUR LINC01470 genic behavior decrease rs9922678 GRIN2A IntraSchizophrenia, 2.00E−09 1.07 26198764 GCST003048 (rs9926303)/ genicchronic EUR (F20) rs7191183/ GRIN2A Intra Autism 2.00E−09 1.0638298[1.04-1.09] 28540026 GCST004521 EUR genic spectrum disorder orschizophrenia rs9922678- GRIN2A Intra Schizophrenia, 3.00E−09 1.065[1.04-1.09] 28991256 GCST004946 A/ASN, genic chronic EUR (F20)rs17504622- GRIN2A Intra Schizophrenia, 3.00E−09 1.238 [1.17-1.31]28991256 GCST004946 T/ASN, genic chronic EUR (F20) rs728022696/LINC01470, Intra Schizophrenia, 3.00E−09 1.24 [1.17-1.31] 23974872GCST002149 EUR GRIA1 genic chronic (F20) rs4765913/ CACNA1C IntraBipolar I, II 3.00E−09 1.13 28072414 GCST003962 EUR genic disorderrs9922678/ GRIN2A Intra Schizophrenia, 3.00E−09 28991256 ASN, EUR genicchronic (F20) rs13176930/ GRIA1, Inter Schizophrenia, 1.00E−09 1.063829826198764 GCST003048 EUR LINC01470 genic chronic (F20) rs117578877-GRIN2B Intra Risk-taking 4.00E−09 30643258 GCST007324 T/EUR genicbehavior rs1501357- HCN1 Intra Schizophrenia, 5.00E−09 1.08E+00[1.05-1.11] 25056061 GCST002539 C/ASN, genic chronic EUR (F20)rs4765914/ CACNA1C Intra Bipolar 5.00E−09 30626913 EUR genic disorderrs1006737/ CACNA1C Intra Attention 5.00E−09 1.071 [1.05-1.10] 23453885GCST001877 EUR genic deficit hyperactivity disorder, unipolardepression, schizophrenia, autism spectrum disorder, bipolar disorderrs9922678- GRIN2A Intra Schizophrenia, 6.00E−09 1.0708 [1.05-1.09]30285260 GCST007201 A/ASN genic chronic (F20) rs10744560- CACNA1C, InterBipolar I 8.00E−09 1.123444 31043756 GCST008115 T CACNA1C- genicdisorder IT3 rs1501357- HCN1 Intra Schizophrenia, 1.00E−08 1.075268729283656 GCST003048 C/EUR genic chronic (F20) rs9922678- GRIN2A IntraSchizophrenia, 1.00E−08 1.062 [1.04-1.08] GCST007201 A/ASN, genicchronic EUR (F20) rs68081839/ GRIA4 Intra Nicotine 2.00E−08 0.6587 unit[0.43-0.89] 30287806 GCST006631 EUR genic dependence increase and majordepression rs28607014- NOS1 Intra Schizophrenia, 2.00E−08 1.0570824[1.04-1.08] 28991256 GCST004946 C/EA (Han genic chronic Chinese) (F20)rs12522290- LINC01470, Inter Autism 2.00E−08 1.08 [1.05-1.11] 28540026GCST004521 C/EUR GRIA1 genic spectrum disorder, schizophrenia rs1006737-CACNA1C Intra Bipolar II 3.00E−08 20351715 GCST000641 A/EUR genicdisorder or major depressive disorder rs73568641- OPRM1 Intra Methadone3.00E−08 0.6808 unit [0.44-0.92] 26377243 GCST004136 C/AFR genicmaintenance, increase heroin addiction rs13170232- GRIA1 IntraSchizophrenia, 5.00E−08 1.098901 26198764 GCST003048 T/EUR genic chronic(F20) rs9292918/ HCN1 Intra Autism 5.00E−08 1.0638298 [1.04-1.1] 28540026 GCST004521 EUR genic spectrum disorder, schizophreniars12522290- GRIA1 Intra Schizophrenia, 5.00E−08 1.08 26198764 GCST003048C genic chronic (F20) rs13008299- TOGARAM2 Intra Diastolic 7.00E−07*0.068 unit 0.033-0.103 24376456 GCST002309 G/EUR genic blood decreasepressure and alcoholism rs117578877- AC007527.1, Inter Schizophrenia,9.00E−07* 1.15 26198764 GCST003048 T/EUR GRIN2B genic chronic (F20)rs11127199- TOGARAM2 Intra Response to 7.00E−06* 11.445 unit  6.82-16.0730552317 GCST007317 G/EUR genic ketamine in increase bipolar disorder ormajor depression (increase, dissociation effects)

TABLE 2B Part 2. Regulatory elements associated with enhancer andsuperenhancer SNPs that have been found in the ketamine adverse eventsub-network. GWAS SNP Repor- Pro- ted/ moters Validated casual humanPopula- (EPDnew); disease enhancer- Superenhancers tion(s) PromIDEnhancers promoter pairs (dbSUPER) eQTL rs1051730- CHRNA3_1 Enhancer:neural stem SE_07079   5E−06, A/ASN, (chr15: progenitor cell, SK-N-SHSE_05923 (Brain- Brain- EUR 78620986- cells; ENCODE(ZLab), HippocampusNucleus 78621045) Ensembl; FANTOM: middle)SE_07917 accumbens eRNA score= 40 (Brain-Inferior (basal temporal ganglia) lobe)SE_32861 (H1-ESC)SE_05045 (Brain-Cingulate gyrus)SE_68844 (H9) rs1051730- CHRNA3_1Enhancer: neural stem SE_07079   5E−06, G/EUR (chr15: progenitor cell,SK-N-SH SE_05923 (Brain- Brain- 78620986- cells; ENCODE(ZLab),Hippocampus Nucleus 78621045) Ensembl; FANTOM: middle)SE_07917 accumbenseRNA score = 40 (Brain-Inferior (basal temporal ganglia) lobe)SE_32861(H1- ESC)SE_05045 (Brain-Cingulate gyrus)SE_68844 (H9) rs1051730-CHRNA3_1 Enhancer: neural stem SE_07079   5E−06, A/EUR (chr15:progenitor cell, SK-N-SH SE_05923 (Brain- Brain- 78620986- cells;ENCODE(ZLab), Hippocampus Nucleus 78621045) Ensembl; FANTOM:middle)SE_07917 accumbens eRNA score = 40 (Brain-Inferior (basaltemporal ganglia) lobe)SE_32861 (H1- ESC)SE_05045 (Brain-Cingulategyrus)SE_68844 (H9) rs1051730- CHRNA3_1 Enhancer: neural stem SE_07079  5E−06, G/EUR (chr15: progenitor cell, SK-N-SH SE_05923 (Brain- Brain-78620986- cells; ENCODE(ZLab), Hippocampus Nucleus 78621045) Ensembl;FANTOM: middle)SE_07917 accumbens eRNA score = 40 (Brain-Inferior (basaltemporal ganglia) lobe)SE_32861 (H1- ESC)SE_05045 (Brain-Cingulategyrus)SE_68844 (H9) rs2155646- Enhancer: H1-hESC, SE_05136 (Brain- 8.2E−111, C/EUR neural stem progenitor Cingulate Brain cell, Braingyrus) Cingulate (chr11:113059055- Gyrus 113059658 (chr11:113188333-113188936, eRNA Score: 482) ENCODE, Ensembl, FANTOM, Vista rs7938812-Enhancer: H1-hESC, SE_05136 (Brain-  1.1E−17, T/EUR neural stemprogenitor Cingulate Brain cell gyrus) Cingulate Gyrus rs3130820/TRIM28_1 Enhancer, Astrocytes, SE_33104 (Brain- 1E−23; EUR (chr19:bipolar neuron, brain; Cingulate Brain 58544407- ENSR00000111889 gyrus)Cingulate 58544466) (chr19:58543000- Gyrus TRIM28_2 58546201); (chr19:ENSR00000594490 58544703- (chr19:58546400- 58544762) 58547001)rs3918226- NOS3_2 Enhancer, Astrocytes,  1.1E−07, T/EUR (chr7: bipolarneuron, brain; Brain 150991010- ENSR00000843798 Cere- 150991069)(chr7:151007800- bellum 151009201); ENSR00000843799 (chr7:151013000-151014601) ENSR00000843800 (chr7:151010400- 151011201) rs3918226/ NOS3_2Enhancer, Astrocytes,  1.1E−07, EA (chr7: bipolar neuron, brain; Brain(Han 150991010- ENSR00000843798 Cere- Chinese) 150991069)(chr7:151007800- bellum 151009201); ENSR00000843799 (chr7:151013000-151014601) ENSR00000843800 (chr7:151010400- 151011201) rs3918226- NOS3_2Enhancer, Astrocytes,  1.1E−07, T/AFR, (chr7: bipolar neuron, brain;Brain AMR, 150991010- ENSR00000843798 Cere- ASN, 150991069)(chr7:151007800- bellum EUR 151009201); ENSR00000843799 (chr7:151013000-151014601) ENSR00000843800 (chr7:151010400- 151011201) rs3918226- NOS3_2Enhancer, Astrocytes,  1.1E−07, T/EUR (chr7: bipolar neuron, brain;Brain 150991010- ENSR00000843798 Cere- 150991069) (chr7:151007800-bellum 151009201); ENSR00000843799 (chr7:151013000- 151014601)ENSR00000843800 (chr7:151010400- 151011201) rs3918226- NOS3_2 Enhancer,Astrocytes,  1.1E−07, T/EUR (chr7: bipolar neuron, brain; Brain150991010- ENSR00000843798 Cere- 150991069) (chr7:151007800- bellum151009201); ENSR00000843799 (chr7:151013000- 151014601) ENSR00000843800(chr7:151010400- 151011201) rs 2155290/ Enhancer: H1-hESC, SE_05136(Brain-  8.2E−111, EUR neural stem progenitor Cingulate Brain cell,Brain gyrus) Cingulate (chr11:113059055- Gyrus 113059658(chr11:113188333- 113188936, eRNA Score: 482) ENCODE, Ensembl, FANTOM,Vista rs18281235 TRIM28_1 Enhancer, Astrocytes, SE_33104 (Brain-  1E−23; 5-A/ASN (chr19: bipolar neuron, brain; Cingulate Brain58544407- ENSR00000111889 gyrus) Cingulate 58544466) (chr19:58543000-Gyrus TRIM28_2 58546201); (chr19: ENSR00000594490 58544703-(chr19:58546400- 58544762) 58547001) rs3918226- NOS3_2 Enhancer,Astrocytes,  1.1E−07, T/EUR (chr7: bipolar neuron, brain; Brain150991010- ENSR00000843798 Cere- 150991069) (chr7:151007800- bellum151009201); ENSR00000843799 (chr7:151013000- 151014601) ENSR00000843800(chr7:151010400- 151011201) rs12764899/ BORCS7_1 Enhancer, SE_04641(Brain-  1.1E−19, EUR (chr10: ENSR00000414241 Anterior Brain 102854213-(chr10:102782600- caudate) Hippo- 102854272) 102782801) SE_06458 (Brain-campus Hippocampus middle) rs11191424- BORCS7_1 Enhancer, SE_04641(Brain-  1.1E−19, G/EUR (chr10: ENSR00000414241 Anterior Brain102854213- (chr10:102782600- caudate) Hippo- 102854272) 102782801)SE_06458 (Brain- campus Hippocampus middle) rs2007044/ Enhancer:H1-hESC, DE_00452: chr12:  3.0E−15, ASN neural stem progenitor2339802-2367400; Brain cell Schizophrenia Cingulate Gyrus rs2159100/Enhancer: H1-hESC, DE_00452: chr12:  3.0E−15, ASN, neural stemprogenitor 2339802-2367400; Brain EUR cell Schizophrenia Cingulate Gyrusrs514465- GABRA2_3 Enhancer, Bipolar 6E−20; A/EUR (chr4: neuron, Brain;Nerve 46389456- GH04J046508: tibial 46389515) GABRA2_1 (chr4: 46389970-46390029) GABRA2_2 (chr4: 46390118- 46390177) rs2007044- Enhancer:H1-hESC, DE_00452: chr12:  3.0E−15, G/ASN, neural stem progenitor2339802-2367400; Brain EUR cell Schizophrenia Cingulate Gyrus rs1024582-Enhancer, Bipolar DE_00452: chr12:  3.0E−15, A/ASN neuron, Brain2339802-2367400; Brain Schizophrenia Cingulate Gyrus rs2007044/Enhancer: H1-hESC, DE_00452: chr12:  3.0E−15, EUR neural stem progenitor2339802-2367400; Brain cell Schizophrenia Cingulate Gyrus rs10774909/Enhancer: H1-hESC, EUR neural stem progenitor cell, SK-N-SH cells;GH12J117579: chr12:118017798- 118019205; ENCODE(ZLab), Ensembl; FANTOM:eRNA score = 3 rs2007044/ Enhancer: H1-hESC, DE_00452: chr12:  3.0E−15,EUR neural stem progenitor 2339802-2367400; Brain cell SchizophreniaCingulate Gyrus rs7192140 GRIN2A_3 Enhancer, Bipolar  1.1E−12, (chr16:neuron, H1 neuronal Brain 10181941- progenitor cells; Frontal 10182000)GH16J010079: Cortex GRIN2A_2 chr16:10173268- (chr16: 10175436; 10182377-ENCODE(ZLab), 10182436) Ensembl, dbSUPER GRIN2A_4 (chr16: 10182692-10182751) GRIN2A_1 (chr16: 10182872- 10182931) rs2239030- Enhancer,Cingulate DE_00452: chr12:   7E−25: A/EUR Gyrus; GH12J002226:2339802-2367400; Brain chr12:2335414-2336748; Schizophrenia CingulatedbSUPER, Gyrus MASTERMIND rs3918226- NOS3_2 Enhancer, Astrocytes, 1.1E−07, T/EA (chr7: bipolar neuron, brain; Brain (Han 150991010-ENSR00000843798 Cere- Chinese) 150991069) (chr7:151007800- bellum151009201); ENSR00000843799 (chr7:151013000- 151014601) ENSR00000843800(chr7:151010400- 151011201) rs11055991- A/EUR rs2007044/ Enhancer:H1-hESC, DE_00452: chr12:  3.0E−15, EUR neural stem progenitor 2339802-2367400; Brain cell Schizophrenia Cingulate Gyrus rs1024582- Enhancer:H1-hESC, DE_00452: chr12:  5.5E−35, A/EUR neural stem progenitor2339802- 2367400; Brain cell Schizophrenia Cingulate Gyrus rs7893279-Enhacer, 2.00E−15; T/ASN, Astrocytes, Bipolar Brain EUR neuron, BrainFrontal Cortex rs8042374- CHRNA3_2 Enhancer: neural stem SE_07079 5.5E−35, A/EA (chr15: progenitor cell, SK-N-SH SE_05923 (Brain- Brain(Han 78621269- cells; ENCODE(ZLab), Hippocampus Cingulate Chinese)78621328) Ensembl; FANTOM: middle)SE_07917 Gryus eRNA score = 40(Brain-Inferior temporal lobe)SE_32861 (H1- ESC)SE_05045(Brain-Cingulate gyrus)SE_68844 (H9) rs7893279- Enhacer, 2.00E−15;T/ASN, Astrocytes, Bipolar Brain EUR neuron, Brain Frontal Cortexrs7893279- Enhacer, 2.00E−15; T, Astrocytes, Bipolar Brain ASN, neuron,Brain Frontal EUR Cortex rs1006737/ Enhancer, Cingulate DE_00452: chr12:  1E−12: EUR Gyrus; GH12J002226: 2339802-2367400; Brainchr12:2335414-2336748; Schizophrenia; 23andMe Frontal dbSUPER blogrs1006737 or Cortex (rs2159100) Each T at this SNP increased the odds ofbipolar disorder by 1.9-fold rs28681284- Enhancer: neural stem SE_07079 5.5E−35, C/ASN progenitor cell, SK-N-SH SE_05923 (Brain- Brain cells;ENCODE(ZLab), Hippocampus Cingulate Ensembl; FANTOM: middle)SE_07917Gryus eRNA score = 40 (Brain-Inferior temporal lobe)SE_32861 (H1-ESC)SE_05045 (Brain-Cingulate gyrus)SE_68844 (H9) rs7893279- Enhacer,2.00E−15; T/ASN, Astrocytes, Bipolar Brain EUR neuron, Brain FrontalCortex rs3918226/ NOS3_2 Enhancer, Astrocytes,  1.1E−07, EUR (chr7:bipolar neuron, brain; Brain 150991010- ENSR00000843798 Cere- 150991069)(chr7:151007800- bellum 151009201); ENSR00000843799 (chr7:151013000-151014601) ENSR00000843800 (chr7:151010400- 151011201) rs7893279-Enhacer, 2.00E−15; T/ASN Astrocytes, Bipolar Brain (Han neuron, BrainFrontal Chinese), Cortex EUR rs1006737- Enhancer, Cingulate DE_00452:chr12:   1E−12: A/EUR Gyrus; GH12J002226: 2339802-2367400; Brainchr12:2335414-2336748; Schizophrenia; 23andMe Frontal dbSUPER blogrs1006737 or Cortex (rs2159100) Each T at this SNP increased the odds ofbipolar disorder by 1.9-fold rs111294930- GRIA1_1 Enhancer, Brain,neural   1E−25; A/EUR (chr5: progenitor cell: Brain 153490621-ENSR00000774341 Cingulate 153490680) (chr5:153490200- Gyrus 153492801)Ensemble, ENCODE. FANTOm rs7688285- GLRB_1 Enhancer, GLRB,   1E−22,C/AFR, (chr4: GRIA2, PDGFC, Amyg- EUR 157076101- Astrocytes, Brain; dala157076160) ENCODE(ZLab), Ensembl, dbSUPER rs641574- GRIA4_1chr11:105609674- SE_05045 (Brain-   1E−25; A/EUR (chr11: 105612903Cingulate Brain 105610024- gyrus) Cingulate 105610083) Gyrus GRIA4_3(chr11: 105610666- 105610725) GRIA4_2 (chr11: 105610842- 105610901)rs2298527- Enhancer: H1-hESC, SE_05136 (Brain-  8.2E−111, C/EUR neuralstem progenitor Cingulate Brain cell, Brain gyrus) Cingulate(chr11:113059055- Gyrus 113059658 (chr11:113188333- 113188936, eRNAScore: 482) ENCODE, Ensembl, FANTOM, Vista rs7192140- GRIN2A_3 Enhancer,Bipolar  1.1E−12, C/EUR (chr16: neuron, H1 neuronal Brain 10181941-progenitor cells; Frontal 10182000) GH16J010079: Cortex GRIN2A_2chr16:10173268- (chr16: 10175436; 10182377- ENCODE(ZLab), 10182436)Ensembl, dbSUPER GRIN2A_4 (chr16: 10182692- 10182751) GRIN2A_1 (chr16:10182872- 10182931) rs9292918- HCN1_1 Enhancer, Bipoar SE_05045 (Brain-  1E−11; G/ASN (chr5: Neuron, Brain, neural Cingulate Frontal 45696271-progenitor cell; gyrus) Cortex 45696330) ENSR00000754146 (chr5:45453802-45454473, Type: Proximal): ENOCDE, Ensmbl, FANTOM rs2155290- Enhancer:H1-hESC, SE 05136 (Brain-  8.2E−111, C/EUR neural stem progenitorCingulate Brain cell, Brain gyrus) Cingulate (chr11:113059055- Gyrus113059658 (chr11:113188333- 113188936, eRNA Score: 482) ENCODE, Ensembl,FANTOM, Vista rs16902086/ HCN1_1 Enhancer, Bipoar SE_05045 (Brain-  1E−11; EUR (chr5: Neuron, Brain, neural Cingulate Brain 45696271-progenitor cell; gyrus) Frontal 45696330) ENSR00000754146 Cortex(chr5:45453802- 45454473, Type: Proximal): ENOCDE, Ensmbl, FANTOMrs7893279- Enhacer, 2.00E−15; T/ASN Astrocytes, Bipolar Brain (Hanneuron, Brain Frontal Chinese), Cortex EUR rs2239030- Enhancer,Cingulate DE_ 00452: chr12:   1E−12: A/EUR Gyrus; GH12J002226:2339802-2367400; Brain chr12:2335414-2336748; Schizophrenia; 23andMeFrontal dbSUPER blog rs1006737 or Cortex (rs2159100) Each T at this SNPincreased the odds of bipolar disorder by 1.9-fold rs1837016- NQO1_1Promoter/Enhancer, — SE_35409 (HepG2) — T/EUR (chr16: Brain, Bipolarneurons, SE_05045 (Brain- 69726550- neural progenitor cells, Cingulate69726609) liver; GH16J069721: Gyrus) NQO1_2 chr16:69755550- (chr16:69762759 ;ENCODE(Z- 69726912- Lab), EPDnew, Ensembl, 69726971) FANTOM5,dbSUPER; Elite rs111294930- GRIA1_1 Enhancer, Brain, neural   1E−25;A/ASN, (chr5: progenitor cell: Brain EUR 153490621- ENSR00000774341Cingulate 153490680) (chr5:153490200- Gyrus 153492801) Ensemble, ENCODE.FANTOM rs62367520/ HCN1_1 Enhancer, Bipoar SE_05045 (Brain-   1E−11; EUR(chr5: Neuron, Brain, neural Cingulate Brain 45696271- progenitor cell;gyrus) Frontal 45696330) ENSR00000754146 Cortex (chr5:45453802-45454473, Type: Proximal): ENOCDE, Ensmbl, FANTOM rs11647445- GRIN2A_3Enhancer, Bipolar  1.1E−12, G/EUR (chr16: neuron, H1 neuronal Brain10181941- progenitor cells; Frontal 10182000) GH16J010079: CortexGRIN2A_2 chr16:10173268- (chr16: 10175436; 10182377- ENCODE(ZLab),10182436) Ensembl, dbSUPER GRIN2A_4 (chr16: 10182692- 10182751) GRIN2A_1(chr16: 10182872- 10182931) rs7893279- Enhacer, 2.00E−15; T/ASNAstrocytes, Bipolar Brain (Han neuron, Brain Frontal Chinese), CortexEUR rs4782271- GRIN2A_3 Enhancer, Bipolar  1.1E−12, A/EUR (chr16:neuron, H1 neuronal Brain 10181941- progenitor cells; Frontal 10182000)GH16J010079: Cortex GRIN2A_2 chr16:10173268- (chr16: 10175436; 10182377-ENCODE(ZLab), 10182436) Ensembl, dbSUPER GRIN2A_4 (chr16: 10182692-10182751) GRIN2A_1 (chr16: 10182872- 10182931) rs7893279- Enhacer,2.00E−15; T/EUR Astrocytes, Bipolar Brain neuron, Brain Frontal Cortexrs73047488/ GRIN2B_1 Enhancer, Bipola SE_33104 (Brain- 3.00E−10; EUR(chr12: Neuron, Brain; Cingulate Amy- 13980410- ENSR00000451168 gyrus)gala 13980469) (chr12:13980400- GRIN2B_2 13982401) Ensembl, (chr12:VISTA, FANTOM 13981966- 13982025) rs3825845- CHRNA3_2 Enhancer: neuralstem SE_07079  5.5E−35, C/ASN (chr15: progenitor cell, SK-N-SH SE_05923(Brain- Brain 78621269- cells; ENCODE(ZLab), Hippocampus Cingulate78621328) Ensembl; FANTOM: middle)SE_07917 Gryus eRNA score = 40(Brain-Inferior temporal lobe)SE_32861 (H1- ESC)SE_05045(Brain-Cingulate gyrus)SE_68844 (H9) rs10744560/ — Enhancer, CingulateDE_00452: chr12:   1E−12: EUR Gyrus; GH12J002226: 2339802-2367400; Brainchr12:2335414-2336748; Schizophrenia; 23andMe Frontal dbSUPER blogrs1006737 or Cortex (rs2159100) Each T at this SNP increased the odds ofbipolar disorder by 1.9-fold rs113551349/ SLC6A9_1 Enhancer, Brain, H1 —SE_05770 — EUR (chr1: neuronal progenitor, H9, SE_06677 (Brain-44031452- H9 neuron progenitor Hippocampus 44031511) cells, H9 neuroncells; middle)SE_08888 SLC6A9_4 Ensembl: (Brain-Mid frontal (chr1:ENSR00000355772 lobe)SE_03147 44032187- (chr1:44016001- (Brain-Angular44032246) 44017400); gyrus)SE_03879 MASTERMIND (Brain-Anteriorcaudate)SE_04765 (Brain-Cingulate gyrus)SE_07718 (Brain-Inferiortemporal lobe) rs2973155- GRIA1_1 Enhancer, Brain, neural   1E−25; C/EUR(chr5: progenitor cell: Brain 153490621- ENSR00000774341 Cingulate153490680) (chr5:153490200- Gyrus 153492801) Ensemble, ENCODE. FANTOMrs13233131/ Enhancer, Bipolar SE_05136 (Brain-   1E−32, EUR Neuron,Brain; Cingulate Brain ENSR00000840912 gyrus) Cingulate (chr7:136997601-Gyrus 136998801 (Ensembl, Vista0 rs758117- Enhancer, Cingulate DE_00452:chr12:   1E−12: C/ASN Gyrus; GH12J002226: 2339802-2367400; Brainchr12:2335414-2336748; Schizophrenia; 23andMe Frontal dbSUPER blogrs1006737 or Cortex (rs2159100) Each T at this SNP increased the odds ofbipolar disorder by 1.9-fold rs872123/ — Enhancer, Brain, neural — —  1E−05, EUR progenitor cells, H1 ESC Brain cells; AanteriorENSR00000627779 Caudate (chr2:170809001- 170814605, Type: Proximal)ENCODE, Z labs rs2973155- GRIA1_1 Enhancer, Brain, neural   1E−25; C/EUR(chr5: progenitor cell: Brain 153490621- ENSR00000774341 Cingulate153490680) (chr5:153490200- Gyrus 153492801) Ensemble, ENCODE. FANTOMrs13176930- GRIA1_1 Enhancer, Brain, neural   1E−25; T/ASN (chr5:progenitor cell: Brain 153490621- ENSR00000774341 Cingulate 153490680)(chr5:153490200- Gyrus 153492801) Ensemble, ENCODE. FANTOM rs9292918/HCN1_1 Enhancer, Bipoar SE_05045 (Brain-   1E−11; EUR (chr5: Neuron,Brain, neural Cingulate Frontal 45696271- progenitor cell; gyrus) Cortex45696330) ENSR00000754146 (chr5:45453802- 45454473, Type: Proximal):ENOCDE, Ensmbl, FANTOM rs1478364- GRIA1_1 Enhancer, Brain, neural  1E−25; T/EUR (chr5: progenitor cell: Brain 153490621- ENSR00000774341Cingulate 153490680) (chr5:153490200- Gyrus 153492801) Ensemble, ENCODE.FANTOM rs9922678 GRIN2A_3 Enhancer, dorsolateral  1.1E−12, (rs9926303)/(chr16: frontal cortex, atrocytes, Brain EUR 10181941- bipolar neurons,neural Frontal 10182000) progenitor cells; Cortex GRIN2A_2 ENCODE,FANTOM5, (chr16: Vista. 10182377- 10182436) GRIN2A_4 (chr16: 10182692-10182751) GRIN2A_1 (chr16: 10182872- 10182931) rs7191183/ GRIN2A_3Enhancer, dorsolateral  1.1E−12, EUR (chr16: frontal cortex, atrocytes,Brain 10181941- bipolar neurons, neural Frontal 10182000) progenitorcells; Cortex GRIN2A_2 ENCODE, FANTOM5, (chr16: Vista. 10182377-10182436) GRIN2A_4 (chr16: 10182692- 10182751) GRIN2A_1 (chr16:10182872- 10182931) rs9922678- GRIN2A_3 Enhancer, dorsolateral  1.1E−12,A/ASN, (chr16: frontal cortex, atrocytes, Brain EUR 10181941- bipolarneurons, neural Frontal 10182000) progenitor cells; Cortex GRIN2A_2ENCODE, FANTOM5, (chr16:Vista. 10182377- 10182436) GRIN2A_4 (chr16:10182692- 10182751) GRIN2A_1 (chr16: 10182872- 10182931) rs17504622-GRIN2A_3 Enhancer, dorsolateral  1.1E−12, T/ASN, (chr16: frontal cortex,atrocytes, Brain EUR 10181941- bipolar neurons, neural Frontal 10182000)progenitor cells; Cortex GRIN2A_2 ENCODE, FANTOM5, (chr16: Vista.10182377- 10182436) GRIN2A_4 (chr16: 10182692- 10182751) GRIN2A_1(chr16: 10182872- 10182931) rs728022696/ GRIA1_1 Enhancer, Brain, neural  1E−25; EUR (chr5: progenitor cell: Brain 153490621- ENSR00000774341Cingulate 153490680) (chr5:153490200- Gyrus 153492801) Ensemble, ENCODE.FANTOM rs4765913/ Enhancer, Cingulate DE_00452: chr12:   1E−12: EURGyrus; GH12J002226: 2339802-2367400; Brain chr12:2335414-2336748;Schizophrenia; 23andMe Frontal dbSUPER blog rs1006737 or Cortex(rs2159100) Each T at this SNP increased the odds of bipolar disorder by1.9-fold rs9922678/ GRIN2A_3 Enhancer, dorsolateral  1.1E−12, ASN,(chr16: frontal cortex, atrocytes, Brain EUR 10181941- bipolar neurons,neural Frontal 10182000) progenitor cells; Cortex GRIN2A_2 ENCODE,FANTOM5, (chr16: Vista. 10182377- 10182436) GRIN2A_4 (chr16: 10182692-10182751) GRIN2A_1 (chr16: 10182872- 10182931) rs13176930/ GRIA1_1Enhancer, Brain, neural   1E−25; EUR (chr5: progenitor cell: Brain153490621- ENSR00000774341 Cingulate 153490680) (chr5:153490200- Gyrus153492801) Ensemble, ENCODE. FANTOM rs117578877- GRIN2B_1 Enhancer,Bipola SE_33104 (Brain- 3.00E−10: T/EUR (chr12: Neuron, Brain; CingulateBrain 13980410- ENSR00000451168 gyrus) Cingulate 13980469)(chr12:13980400- Gyrus GRIN2B_2 13982401) Ensembl, (chr12: VISTA, FANTOM13981966- 13982025) rs1501357- HCN1_1 Enhancer, Bipoar SE_05045 (Brain-  1E−11; C/ASN, (chr5: Neuron, Brain, neural Cingulate Frontal EUR45696271- progenitor cell; gyrus) Cortex 45696330) ENSR00000754146(chr5:45453802- 45454473, Type: Proximal): ENOCDE, Ensmbl, FANTOMrs4765914/ Enhancer, Cingulate DE_00452: chr12:   1E−12: EUR Gyrus;GH12J002226: 2339802-2367400; Brain chr12:2335414-2336748;Schizophrenia; 23andMe Frontal dbSUPER blog rs1006737 or Cortex(rs2159100) Each T at this SNP increased the odds of bipolar disorder by1.9-fold rs1006737/ Enhancer, Cingulate DE_00452: chr12:   1E−12: EURGyrus; GH12J002226: 2339802-2367400; Brain chr12:2335414-2336748;Schizophrenia; 23andMe Frontal dbSUPER blog rs1006737 or Cortex(rs2159100) Each T at this SNP increased the odds of bipolar disorder by1.9-fold rs9922678- GRIN2A_3 Enhancer, dorsolateral  1.1E−12, A/ASN(chr16: frontal cortex, atrocytes, Brain 10181941- bipolar neurons,neural Frontal 10182000) progenitor cells; Cortex GRIN2A_2 ENCODE,FANTOM5, (chr16: Vista. 10182377- 10182436) GRIN2A_4 (chr16: 10182692-10182751) GRIN2A_1 (chr16: 10182872- 10182931) rs10744560- Enhancer,Cingulate DE_00452: chr12:   1E−12: T Gyrus; GH12J002226:2339802-2367400; Brain chr12:2335414-2336748; Schizophrenia; 23andMeFrontal dbSUPER blog rs1006737 or Cortex (rs2159100) Each T at this SNPincreased the odds of bipolar disorder by 1.9-fold rs1501357- HCN1_1Enhancer, Bipoar SE_05045 (Brain-   1E−11; C/EUR (chr5: Neuron, Brain,neural Cingulate Frontal 45696271- progenitor cell; gyrus) Cortex45696330) ENSR00000754146 (chr5:45453802- 45454473, Type: Proximal):ENOCDE, Ensmbl, FANTOM rs9922678- GRIN2A_3 Enhancer, dorsolateral 1.1E−12, A/ASN, (chr16: frontal cortex, atrocytes, Brain EUR 10181941-bipolar neurons, neural Frontal 10182000) progenitor cells; CortexGRIN2A_2 ENCODE, FANTOM5, (chr16: Vista. 10182377- 10182436) GRIN2A_4(chr16: 10182692- 10182751) GRIN2A_1 (chr16: 10182872- 10182931)rs68081839/ GRIA4_1 chr11:105609674- SE_05045 (Brain-   1E−25; EUR(chr11: 105612903 Cingulate Brain 105610024- gyrus) Cingulate 105610083)Gyrus GRIA4_3 (chr11: 105610666- 105610725) GRIA4_2 (chr11: 105610842-105610901) rs28607014- Enhancer: H1-hESC, C/EA neural stem progenitor(Han cell, SK-N-SH cells; Chinese) GH12J117579: chr12:118017798-118019205; ENCODE(ZLab), Ensembl; FANTOM: eRNA score = 3 rs12522290-GRIA1_1 Enhancer, Brain, neural   1E−25; C/EUR (chr5: progenitor cell:Brain 153490621- ENSR00000774341 Cingulate 153490680) (chr5:153490200-Gyrus 153492801) Ensemble, ENCODE. FANTOM rs1006737- Enhancer, CingulateDE_00452: chr12:   1E−12: A/EUR Gyrus; GH12J002226: 2339802-2367400;Brain chr12:2335414-2336748; Schizophrenia; 23andMe Frontal dbSUPER blogrs1006737 or Cortex (rs2159100) Each T at this SNP increased the odds ofbipolar disorder by 1.9-fold rs73568641- OPRM1_2 Enhancer, BipolarSE_03879 (Brain-   1E−30: C/AFR (chr6: Neurons, Brain; Anterior Brain154039211- ENSR00000810176 caudate Nucleus 154039270) (chr6:154039001-Acumbens OPRM1_3 154040001) Ensembl, (chr6: ENCODE, FANTOM 154039315-154039374) OPRM1_4 (chr6: 154039500- 154039559) rs13170232- GRIA1_1Enhancer, Brain, neural   1E−25; T/EUR (chr5: progenitor cell: Brain153490621- ENSR00000774341 Cingulate 153490680) (chr5:153490200- Gyrus153492801) Ensemble, ENCODE. FANTOM rs9292918/ HCN1_1 Enhancer, BipoarSE_05045 (Brain-   1E−11; EUR (chr5: Neuron, Brain, neural CingulateFrontal 45696271- progenitor cell; gyrus) Cortex 45696330)ENSR00000754146 (chr5:45453802- 45454473, Type: Proximal): ENOCDE,Ensmbl, FANTOM rs12522290- GRIA1_1 Enhancer, Brain, neural   1E−25; C(chr5: progenitor cell: Brain 153490621- ENSR00000774341 Cingulate153490680) (chr5:153490200- Gyrus 153492801) Ensemble, ENCODE. FANTOMrs13008299- WDR43_1 Enahancer, Brain, H1- SE_05045 (Brain- G/EUR (chr2:hESC, H9, neural stem Cingulate 28894641- progenitor cell; gyrus)28894700) ENSR00000114662 (chr2:28893601- 28897801) Ensembl, ENOCDE,FANTOM, VISTA rs117578877- GRIN2B_1 Enhancer, Bipolar SE_33104 (Brain-3.00E−10; T/EUR (chr12: Neuron, Brain; Cingulate Amy- 13980410-ENSR00000451168 gyrus) gala 13980469) (chr12:13980400- GRIN2B_213982401) Ensembl, (chr12: VISTA, FANTOM 13981966- 13982025) rs11127199-WDR43_1 Enahancer, Brain, H1- SE_05045 (Brain- G/EUR (chr2: hESC, H9,neural stem Cingulate 28894641- progenitor cell; gyrus) 28894700)ENSR00000114662 (chr2:28893601- 28897801) Ensembl, ENOCDE, FANTOM, VISTA

TABLE 2B Part 3. Chromatin interactions for enhancer and superenhancerSNPs that have been found in the ketamine adverse event sub-network.Judged by machine learning to be causal in neural Enhancer cell GWAS SNPRNA co- lines Reported/ HGREEN expression but not Population(s) Hi-CScore score p-value HepG2? rs1051730-A/ 1E−27: Nucleus accumbens1.00E−15 1.00E−50 Yes ASN, EUR rs1051730-G/ 1E−27: Nucleus accumbens1.00E−15 1.00E−50 Yes EUR rs1051730-A/ 1E−27: Nucleus accumbens 1.00E−151.00E−50 Yes EUR rs1051730-G/ 1E−27: Nucleus accumbens 1.00E−15 1.00E−50Yes EUR rs2155646-C/ 1E−35: Cingulate cortex 1.00E−10 1.00E−14 Yes EURrs7938812-T/ 1E−35: Cingulate cortex 1.00E−10 1.00E−10 Yes EURrs3130820/ 1E−10: Cingulate cortex 1.00E−08 1.56E−10 Yes EURrs3918226-T/ 1E.00E−20: Cerebellum 1.00E−08 Yes EUR rs3918226/1E.00E−20: Cerebellum 1.00E−08 Yes EA (Han Chinese) rs3918226-T/1E.00E−20: Cerebellum 1.00E−08 Yes AFR, AMR, ASN, EUR rs3918226-T/1E.00E−20: Cerebellum 1.00E−08 Yes EUR rs3918226-T/ 1E.00E−20:Cerebellum 1.00E−08 Yes EUR rs2155290/ 1E−35: Cingulate cortex 1.00E−101.00E−14 Yes EUR rs182812355-A/ 1E−10: Cingulate cortex 1.00E−081.56E−10 Yes ASN rs3918226-T/ 1E.00E−20: Cerebellum Yes EUR rs12764899/0.00000001: Hippocampus 1.00E−11 5.00E−34 Yes EUR rs11191424-G/0.00000001: Hippocampus 1.00E−11 5.00E−34 Yes EUR rs2007044/0.0000000001: Cingulate cortex 1.00E−15 1.30E−89 Yes ASN rs2159100/0.0000000001: Cingulate cortex 1.00E−30 1.00E−14 Yes ASN EUR rs514465-A/0.0000000000001: Frontal cortex 1.00E−15 Yes EUR rs2007044-G/0.0000000001: Cingulate cortex 1.00E−30 1.30E−89 Yes ASN, EURrs1024582-A/ 0.0000000001: Cingulate cortex 1.00E−30 Yes ASN rs2007044/0.0000000001: Cingulate cortex 1.00E−15 Yes EUR rs10774909/ 0.0000001:Frontal cortex 1.00E−10 1.00E−02 Yes EUR rs2007044/ 0.0000000001:Cingulate cortex 1.00E−30 1.30E−89 Yes EUR rs71921400.0000000000000000001: Frontal cortex 1.00E−20 3.00E−12 Yes rs2239030-A/0.0000000001: Cingulate cortex 1.00E−15 1.00E−03 Yes EUR rs3918226-T/1E.00E−20: Cerebellum 1.00E−08 Yes EA (Han Chinese) rs11055991-A/ 0.01:Frontal cortex 1.00E−12 EUR rs2007044/ 0.0000000001: Cingulate cortex1.00E−15 1.30E−89 Yes EUR rs1024582-A/ 0.0000000001: Cingulate cortex1.00E−15 1.00E−17 Yes EUR rs7893279-T/ 0.0000000001: Frontal cortex1.00E−15    E−08 Yes ASN, EUR rs8042374-A/ 1E−27: Cingulate cortex1.00E−27 1.00E−50 Yes EA (Han Chinese) rs7893279-T/ 0.000001; Frontalcortex 1.00E−05    E−23 ASN, EUR rs7893279-T, 0.000001; Frontal cortex1.00E−05    E−23 Yes ASN, EUR rs1006737/ 0.0000000001: Cingulate cortex1.00E−10 1.00E−03 Yes EUR rs28681284-C/ 1E−27: Nucleus accumbens1.00E−27 1.00E−50 No ASN rs7893279-T/ 0.000001; Frontal cortex 1.00E−151.00E−08 Yes ASN, EUR rs3918226/ 1E.00E−20: Cerebellum 1.00E−08 Yes EURrs7893279-T/ 0.000001: Frontal cortex 1.00E−15    E−08 Yes ASN (HanChinese), EUR rs1006737-A/ 0.0000000001: Cingulate cortex 1.00E−101.00E−03 Yes EUR rs111294930-A/ 0.000000000001: Cingulate cortex1.00E−25 1.25E−19 Yes EUR rs7688285-C/ 0.00000000001: Amygdala Yes AFR,EUR rs641574-A/ 0.000000001: Cingulate cortex 1.00E−41 1.25E−27 Yes EURrs2298527-C/ 1E−35: Cingulate cortex 1.00E−10 1.00E−14 Yes EURrs7192140-C/ 0.0000000000000000001: Frontal cortex 1.00E−20 3.00E−12 YesEUR rs9292918-G/ 0.0001: Frontal cortex 1.00E−05 Yes ASN rs2155290-C/1E−35: Cingulate cortex 1.00E−10 1.00E−14 Yes EUR rs16902086/ 0.0001:Frontal cortex 1.00E−05 Yes EUR rs7893279-T/ 0.000001: Frontal cortex1.00E−15 1.00E−08 ASN (Han Chinese), EUR rs2239030-A/ 0.0000000001:Cingulate cortex 1.00E−20 1.00E−03 Yes EUR rs1837016-T/ 0.001: Cingulatecortex — — Yes EUR rs111294930-A/ 0.000000000001; Cingulate cortex1.00E−25 1.25E−19 Yes ASN, EUR rs62367520/ 0.0001: Frontal cortex1.00E−05 Yes EUR rs11647445-G/ 0.0000000000000000001: Frontal cortex1.00E−20 3.00E−12 Yes EUR rs7893279-T/ 0.000001: Frontal cortex 1.00E−151.00E−08 Yes ASN (Han Chinese), EUR rs4782271-A/ 0.0000000000000000001:Frontal cortex 1.00E−20 3.00E−12 Yes EUR rs7893279-T/ 1.00E−10 1.00E−151.00E−08 Yes EUR rs73047488/ 0.0000000001: Cingulate cortex 1.00E−151.00E−33 Yes EUR rs3825845-C/ 1E−27: Nucleus accumbens 1.00E−27 1.00E−50Yes ASN rs10744560/ 0.0000000001: Frontal cortex 1.00E−20 1.00E−03 YesEUR rs113551349/ EUR 0.0000000000001: Frontal cortex 2.00E−20 1.00E−53Yes rs29731 55-C/ 0.000000000001: Cingulate cortex 1.00E−25 1.25E−19 YesEUR rs13233131/ 0.00001: Cingulate cortex 1.00E−10 5.00E−12 Yes EURrs758117-C/ 0.0000000001: Frontal cortex 1.00E−20 1.00E−03 Yes ASNrs872123/ 0.0000000001: Cingulate cortex 1.00E−10  1.2E−15; Yes EURTibial nerve rs29731 55-C/ 0.000000000001: Cingulate cortex 1.00E−251.25E−19 Yes EUR rs13176930-T/ 0.000000000001: Cingulate cortex 1.00E−251.25E−19 Yes ASN rs9292918/ 0.0001: Frontal cortex 1.00E−05 Yes EURrs1478364-T/ 0.000000000001: Cingulate cortex 1.00E−25 1.25E−19 EURrs9922678 0.0000000000000000001: Frontal cortex 1.00E−20 3.00E−12 Yes(rs9926303)/ EUR rs7191183/ 0.0000000000000000001: Frontal cortex1.00E−20 3.00E−12 Yes EUR rs9922678-A/ 0.0000000000000000001: Frontalcortex 1.00E−20 3.00E−12 ASN, EUR rs17504622-T/ 0.0000000000000000001:Frontal cortex 1.00E−20 3.00E−12 Yes ASN, EUR rs728022696/0.000000000001: Cingulate cortex 1.00E−25 1.25E−19 Yes EUR rs4765913/0.0000000001: Frontal cortex 1.00E−20 1.00E−03 Yes EUR rs9922678/0.0000000000000000001: Frontal cortex 1.00E−20 3.00E−12 Yes ASN, EURrs13176930/ 0.000000000001: Cingulate cortex 1.00E−25 1.25E−19 Yes EURrs117578877-T/ 0.000000000000000000000000000000000001: Cingulate cortex1.00E−15 1.00E−33 Yes EUR rs1501357-C/ 0.0001: Frontal cortex 1.00E−05Yes ASN, EUR rs4765914/ 0.0000000001: Frontal cortex 1.00E−20 1.00E−03Yes EUR rs1006737/ 0.0000000001: Frontal cortex 1.00E−10 1.00E−03 YesEUR rs9922678-A/ 0.0000000000000000001: Frontal cortex 1.00E−20 3.00E−12Yes ASN rs10744560-T 0.0000000001: Frontal cortex 1.00E−20 1.00E−03 Yesrs1501357-C/ 0.0001: Frontal cortex 1.00E−05 — Yes EUR rs9922678-A/0.0000000000000000001: Frontal cortex 1.00E−20 3.00E−12 Yes ASN, EURrs68081839/ 0.0000000001: Cingulate cortex 1.00E−41 1.25E−27 Yes EURrs28607014-C/ 0.0000001: Frontal cortex 1.00E−10 1.00E−02 Yes EA (HanChinese) rs12522290-C/ 0.000000000001: Cingulate cortex 1.00E−251.25E−19 Yes EUR rs1006737-A/ 0.0000000001: Frontal cortex 1.00E−101.00E−03 Yes EUR rs73568641-C/ 0.000000000000001: Nucleus accumbens1.00E−10 1.00E−41 Yes AFR rs13170232-T/ 0.000000000001: Cingulate cortex1.00E−25 1.25E−19 Yes EUR rs9292918/ 0.0001: Frontal cortex 1.00E−05 YesEUR rs12522290-C 0.000000000001: Cingulate cortex 1.00E−25 1.25E−19 Yesrs13008299-G/ 0.0001: Cingulate cortex Yes EUR rs117578877-T/0.0000000001: Amygdala 1.00E−15 1.00E−33 Yes EUR rs11127199-G/ 0.0001:Cingulate cortex Yes EUR

In another embodiment of the methods in this disclosure, generalizationof this method may be used to reveal combinations of FDA-approvedmedications that may be used to enhance therapeutics in neuropsychiatricdisorders through their corresponding network or sub-network mechanismsin biology. FIG. 22 illustrates an example of how the valproic acidpharmacogenomic network and the ketamine pharmacogenomic network work ina complementary manner to support neurogenesis. Valproic acid inducesthe conversion of neural progenitor cells to committed neuralprogenitors through the npBAF complex (1, top), and ketamine may act oncommitted neural progenitors through the human silencing complex (HUSH)to turn progenitors into differentiation neurons (1, bottom).

FIG. 23 illustrates how the process shown in FIG. 22 acts throughprogressive deacetylation of the histone 3 lysine 9 (H3K9) moiety ascaused by the valproic pharmacogenomic network (FIG. 23A), andacetylation of the H3K9 moiety following activation of the ketaminepharmacogenomic network (FIG. 23B).

FIG. 24 illustrates how the complementary pharmacogenomic networks ofvalproic acid and ketamine bring neural progenitors to become mature,differentiated neurons.

In another embodiment of this disclosure, these methods can be used forother antidepressant medications that target the NMDAR network. Forexample, other NMDAR partial antagonists, including AVP-786 and GLYX-13(RapastineI), are in clinical trials as antidepressant medications.Also, blockers of the GLRB on the NMDAR are also under development asantidepressants, including AV101 and D-cycloserine (Seromycin).Selective antagonists of the GRIN2B of the NMDAR are also in developmentas antidepressants, examples including EVT103, CP101 and MK-0657.Downstream in this pathway is the AMPAR, and several antidepressants arebeing developed as agonists at GRIA1 and GRIA2 such as ORG 265576.

In another embodiment, these methods can be used for optimization ofmedication selection for other antidepressants providing greater powerthan commercially available pharmacogenomic clinical decision supportassays that just rely on coding SNPs for classifying patients as tomedication. Methods covered by the techniques disclosed herein exploitknowledge of the pharmacogenomic epigenome, including its organizationinto TADs and TAD-TAD pharmacogenomic connections, which provideenhanced insight into CNS drug mechanisms. In addition, these methodspermit objective monitoring of drug-drug interactions and dosing, aswell as measurement of parent drugs and their metabolites from serum, asis the case for S-ketamine and its active metabolite nor-ketamine toprovide additional insight into individual metabolizer subtypes.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium or in a transmission signal) or hardware.In hardware, the routines, etc., are tangible units capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

This detailed description is to be construed as providing examples onlyand does not describe every possible embodiment, as describing everypossible embodiment would be impractical, if not impossible. One couldimplement numerous alternate embodiments, using either currenttechnology or technology developed after the filing date of thisapplication.

We claim:
 1. A computing device for determining a drug or drugs toadminister to a patient suffering from a neuropsychiatric, neurological,or analgesia/pain disorder, the computing device comprising: acommunication network, one or more processors; and a non-transitorycomputer-readable memory coupled to the one or more processors andstoring thereon instructions that, when executed by the one or moreprocessors, cause the computing device to: obtain a biological sample ofa patient; analyze the biological sample using one or more of: targetedsingle nucleotide polymorphisms (SNP) genotyping, RNA sequencing, andchromatin conformation capture based on the differential activation andrepression of topologically associating domains (TADs) in the patientindicative of drug efficacy and adverse drug events; determine apharmacogenomic network representation for a drug gene set for aglutamate N-methyl-d-aspartate receptor (NMDAR) antagonist or partialantagonist, a glycine receptor beta (GLRB) modulator, or anα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)agonist for the patient based on the analysis; compare thepharmacogenomic network representation for the drug gene set for theNMDAR antagonist or partial antagonist, GLRB modulator, or AMPAR agonistfor the patient to a drug pharmacogenomic network and constituentsub-networks for the NMDAR antagonist or partial antagonist, GLRBmodulator, or AMPAR agonist from a reference database; determine toadminister the NMDAR antagonist or partial antagonist, GLRB modulator,or AMPAR agonist for administering to the patient based on thecomparison; and cause the NMDAR antagonist or partial antagonist, GLRBmodulator, or AMPAR agonist to be administered to the patient.
 2. Thecomputing device of claim 1, wherein: the pharmacogenomic networkrepresentation for the drug gene set for the NMDAR antagonist or partialantagonist, GLRB modulator, or AMPAR agonist for the patient includes apatient drug pharmacodynamic efficacy sub-network, a patient drugpharmacodynamic adverse event sub-network, a chromatin remodelingsub-network, and a pharmacokinetic enzymes and hormones sub-network, andthe constituent sub-networks for the NMDAR antagonist or partialantagonist, GLRB modulator, or AMPAR agonist from the reference databaseincludes a set of reference drug pharmacodynamic efficacy sub-networks,reference drug pharmacodynamic adverse event sub-networks, referencechromatin remodeling sub-networks, and reference pharmacokinetic enzymesand hormones sub-networks spanning human drug response variation, and tocompare the pharmacognemoic representation for the drug gene set for theNMDAR antagonist or partial antagonist, GLRB modulator, or AMPAR agonistfor the patient to a drug pharmacogenomic network and constituentsub-networks for the NMDAR antagonist or partial antagonist, GLRBmodulator, or AMPAR agonist from a reference database, the instructionscause the computing device to: assign a first score to the patient drugpharmacodynamic efficacy sub-network based on an amount of similaritybetween the patient drug pharmacodynamic efficacy sub-network and theset of reference drug pharmacodynamic efficacy sub-networks; assign asecond score to the patient drug pharmacodynamic adverse eventsub-network based on an amount of similarity between the patient drugpharmacodynamic adverse event sub-network and the set of reference drugpharmacodynamic adverse-event sub-networks.
 3. The computing device ofclaim 2, wherein to determine to administer the NMDAR antagonist orpartial antagonist, GLRB modulator, or AMPAR agonist for administeringto the patient based on the comparison, the instructions cause thecomputing device to: determine to administer the NMDAR antagonist orpartial antagonist, GLRB modulator, or AMPAR agonist for the patientwhen the first score is above a first threshold score or the secondscore is below a second threshold score.
 4. The computing device ofclaim 3, wherein to determine to administer the NMDAR antagonist orpartial antagonist, GLRB modulator, or AMPAR agonist for the patientwhen the first score is above a first threshold score or the secondscore is below a second threshold score, the instructions cause thecomputing device to: combine the first and second scores to generate anoverall score; and determine to administer the NMDAR antagonist orpartial antagonist, GLRB modulator, or AMPAR agonist for the patientwhen the overall score is above a third threshold score.
 5. Thecomputing device of claim 1, wherein the instructions further cause thecomputing device to: determine a dosage of the NMDAR antagonist orpartial antagonist, GLRB modulator, or AMPAR agonist for administeringto the patient based on the comparison; and cause the determined dosageof the NMDAR antagonist or partial antagonist, GLRB modulator, or AMPARagonist to be administered to the patient.
 6. The computing device ofclaim 5, wherein to determine a dosage of the NMDAR antagonist orpartial antagonist, GLRB modulator, or AMPAR agonist for administeringto the patient, the instructions cause the computing device to:determine the dosage using a regression model based on a combination oftwo or more of: the sex of the patient, the age of the patient, whetherthe patient smokes, ethnicity of the patient, height of the patient,weight of the patient, and mental illness history of the patient.
 7. Thecomputing device of claim 5, wherein the instructions further cause thecomputing device to: obtain clinical data for the patient; analyze thebiological sample using pharmacometabolomics to determine pre-existingmedications and metabolites of the pre-existing medications in thepatient; and determine whether to administer the NMDAR antagonist orpartial antagonist, GLRB modulator, or AMPAR agonist to the patient orthe dosage to administer based at least in part on drug-gene ordrug-drug interactions between the pre-existing medications in thepatient and the NMDAR antagonist or partial antagonist, GLRB modulator,or AMPAR agonist.
 8. The computing device of claim 1, wherein the drugpharmacogenomic network for the NMDAR antagonist or partial antagonist,GLRB modulator, or AMPAR agonist from the reference database is aketamine pharmacogenomic network and includes one or more of: Activityregulated cytoskeleton associated protein (ARC) gene, Achaete-Scutefamily bHLH transcription factor 1 (ASCL1) gene, Brain derivedneurotrophic factor (BDNF) gene, BDNF antisense RNA (BDNF-AS) gene,Calcium/calmodulin dependent protein kinase II alpha (CAMK2A) gene,Cyclin dependent kinase inhibitor 1A (CDKN1A) gene, cAMP responsiveelement modulator (CREM) gene, Cut like homeobox 2 (CUX2) gene, DCCnetrin 1 receptor (DCC) gene, Dopamine receptor D2 (DRD2) gene,Eukaryotic translation elongation factor 2 kinase (EEF2K) gene, FragileX mental retardation 1 (FMR1) gene, Ganglioside induced differentiationassociated protein 1 like 1 (GDAP1L1) gene, Glutamate metabotropicreceptor 5 (GRM5) gene, Homer scaffold protein 1 (HOMER1) gene,5-hydroxytryptamine receptor 1B (HTR1B) gene, 5-hydroxytryptaminereceptor 2A (HTR2A) gene, Kruppel like factor 6 (KLF6) gene, Lin-7homolog C, crumbs cell polarity complex component (LIN7C) long noncodingRNA, LOC105379109 long noncoding RNA, Myocyte enhancer factor 2D (MEF2D)gene, Myosin VI (MYO6) gene, Myelin transcription factor 1 like (MYT1L)gene, Neuronal differentiation 1 (NEUROD1) gene, Neuronaldifferentiation 2 (NEUROD2) gene, Nescient helix-loop-helix 2 (NHLH2)gene, Neuromedin B (NMB) gene, NMDA receptor synaptonuclear signalingand neuronal migration factor (NSMF) gene, Neurotrophic receptortyrosine kinase 2 (NTRK2) gene, Phosphotase and tensin homolog (PTEN)gene, Prostaglandin-endoperoxide synthase 2 (PTGS2) gene, Rac familysmall GTPase 1 (RAC1) gene, Ras protein specific guanine nucleotidereleasing factor 2 (RASGRF2) gene, Ras homolog family member A (RHOA)gene, Roundabout guidance receptor 2 (ROBO2) gene, RP11_360A181 longnoncoding RNA, Semaphorin 3A (SEMA3A) gene, SH3 and multiple ankyrinrepeat domains 1 (SHANK1) gene, SH3 and multiple ankyrin repeat domains2 (SHANK2) gene, SH3 and multiple ankyrin repeat domains 3 (SHANK3)gene, Solute carrier family 22 member 15 (SLC22A15) gene, Solute carrierfamily 6 member 2 (SLC6A2) gene, Slit guidance ligand 1 (SLIT1) gene,Slit guidance ligand 2 (SLIT2) gene, Synaptosome associated protein 25(SNAP25) gene, Synapsin I (SYN1) gene, Synapsin II (SYN2) gene, SynapsinIII (SYN3) gene, T-box, brain 1 (TBR1) gene, Transcription factor 4(TCF4) gene, Acetylcholinesterase (ACHE) gene, Activating transcriptionfactor 7 interacting protein (ATF7IP) gene, Activating transcriptionfactor 7 interacting protein 2 (ATF7IP2) gene, ATPase Na+/K+Transporting Subunit Alpha 1 (ATP1A1) gene, BLOC-1 related complex unit7 (BORCS7) gene, Bromodomain containing 4 (BRD4) gene, Calciumvoltage-gated channel subunit alpha1 C (CACNA1C) gene, Calciumvoltage-gated channel auxiliary subunit beta 1 (CACNB1) gene, Calciumvoltage-gated channel auxiliary subunit beta 2 (CACNB2) gene, Calciumvoltage-gated channel auxiliary subunit gamma 2 (CACNG2) gene,Cholinergic Receptor Muscarinic 2 (CHRM2) gene, Cholinergic ReceptorNicotinic Alpha 3 Subunit (CHRNA3) gene, Cholinergic Receptor NicotinicAlpha 5 Subunit (CHRNA5) gene, Cholinergic Receptor Nicotinic Alpha 7Subunit (CHRNA7) gene, Cannabinoid receptor 1 (CNR1) gene, Disks largehomolog 3 (DLG3) gene, Disks large homolog 4 (DLG4) gene, DNAMethyltransferase 1 (DNMT1) gene, Euchromatic histone lysinemethyltransferase 1 (EHMT1) gene, Gamma-aminobutyric acid type Areceptor alpha2 subunit (GABRA2) gene, Gamma-aminobutyric acid type Areceptor alpha5 subunit (GABRA5) gene, Glutamate decarboxylase 1 (GAD1)gene, Glycine receptor alpha 1 (GLRA1) gene, Glycine receptor alpha 2(GLRA2) gene, Glycine receptor beta (GLRB) gene, Glutamate ionotropicreceptor AMPA type subunit 1 (GRIA1) gene, Glutamate ionotropic receptorAMPA type subunit 2 (GRIA2) gene, Glutamate ionotropic receptor AMPAtype subunit 2 (GRIA4) gene, Glutamate ionotropic receptor NMDA typesubunit 1 (GRIN1) gene, Glutamate ionotropic receptor NMDA type subunit2A (GRIN2A) gene, Glutamate ionotropic receptor NMDA type subunit 2B(GRIN2B) gene, Glutamate ionotropic receptor NMDA type subunit 2C(GRIN2C) gene, Glutamate ionotropic receptor NMDA type subunit 2D(GRIN2D) gene, Glutamate ionotropic receptor NMDA type subunit 3A(GRIN3A) gene, Glutamate ionotropic receptor NMDA type subunit 3B(GRIN3B) gene, Hyperpolarization Activated Cyclic Nucleotide GatedPotassium Channel 1 (HCN1) gene, Histone deacetylase 5 (HDAC5) gene,Methyl-CpG binding domain protein 1 (MBD1) gene, M-Phase Phosphoprotein8 (MPHOSPH8) gene, Neural cell adhesion molecule 1 (NCAM1) gene, Nitricacid synthase 1 (NOS1) gene, Nitric acid synthase 2 (NOS2) gene, Nitricacid synthase 3 (NOS3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1)gene, Opioid receptor kappa 1 (OPRK1) gene, Opioid receptor mu 1 (OPRM1)gene, Roundabout guidance receptor 2 (ROBO2) gene, SET domain bifurcated1 (SETDB1) gene, SH3 and Multiple Ankyrin Repeat Domains 2 (SHANK2)gene, Sigma Non-Opioid Intracellular Receptor 1 (SIGMAR1) gene, Solutecarrier family 6 member 9 (SLC6A9) gene, Transcription ActivationSuppressor (TASOR) gene, TOG array regulator of axonemal microtubules 2(TOGORAM2) gene, Tripartite Motif Containing 28 (TRIM28) gene, ZincFinger Protein 274 (ZNF274) gene, Anaphase promoting complex subunit 2(ANAPC2) gene, Cytochrome P450 family 2 subfamily A member 6 (CYP2A6)gene, Cytochrome P450 family 2 subfamily B member 6 (CYP2B6) gene,Cytochrome P450 family 3 subfamily A member 4 (CYP3A4) gene, Disks largehomolog 4 (DLG4), Eukaryotic Elongation Factor 2 Kinase (EEF2K) gene,Estrogen Receptor 1 (ESR1) gene, Glutamate ionotropic receptor AMPA typesubunit 1 (GRIA1) gene, Glutamate ionotropic receptor AMPA type subunit2 (GRIA4) gene, Glutamate ionotropic receptor NMDA type subunit 1(GRIN1) gene, Glutamate ionotropic receptor NMDA type subunit 2B(GRIN2B) gene, Myosin VI (MYO6) gene, Roundabout Guidance Receptor 2(ROBO2) gene, SH3 and Multiple Ankyrin Repeat Domains 2 (SHANK2) gene,or Transcription Elongation Regulator 1 (TCERG1) gene.
 9. A method fortreating a patient suffering from neuropsychiatric, neurological, oranalgesia/pain disorders, the method comprising: obtaining a biologicalsample of a patient; analyzing or having analyzed the biological sampleusing one or more of: RNA sequencing or expression microarray analysis,chromatin conformation capture, or targeted genotyping based ondifferential activation and repression of topologically associatingdomains (TADs) in the biological sample of the patient indicative ofdrug efficacy and adverse drug events; determining or having determineda pharmacogenomic network representation for a drug gene set for aglutamate N-methyl-d-aspartate receptor (NMDAR) antagonist or partialantagonist, a glycine receptor beta (GLRB) modulator, or anα-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)agonist for the patient based on the analysis; comparing or havingcompared the pharmacogenomic network representation for the drug geneset for the NMDAR antagonist or partial antagonist, GLRB modulator, orAMPAR agonist for the patient to a drug pharmacogenomic network andconstituent sub-networks for the NMDAR antagonist or partial antagonist,GLRB modulator, or AMPAR agonist from a reference database; determiningto administer the NMDAR antagonist or partial antagonist, GLRBmodulator, or AMPAR agonist for administering to the patient based onthe comparison; and administering the NMDAR antagonist or partialantagonist, GLRB modulator, or AMPAR agonist to the patient.
 10. Themethod of claim 9, wherein: the pharmacogenomic network representationfor the drug gene set for the NMDAR antagonist or partial antagonist,GLRB modulator, or AMPAR agonist for the patient includes a patient drugpharmacodynamic efficacy sub-network, a patient drug pharmacodynamicadverse event sub-network, a chromatin remodeling sub-network, and apharmacokinetic enzymes and hormones sub-network and the constituentsub-networks for the NMDAR antagonist or partial antagonist, GLRBmodulator, or AMPAR agonist from the reference database includes a setof reference drug pharmacodynamic efficacy sub-networks, reference drugpharmacodynamic adverse event sub-networks, reference chromatinremodeling sub-networks, and reference pharmacokinetic enzymes andhormones sub-networks, and comparing the pharmacogenomic networkrepresentation for the drug gene set for the NMDAR antagonist or partialantagonist, GLRB modulator, or AMPAR agonist for the patient to a drugpharmacogenomic network and constituent sub-networks for the NMDARantagonist or partial antagonist, GLRB modulator, or AMPAR agonist froma reference database includes: assigning a first score to the patientdrug efficacy sub-network based on an amount of similarity between thepatient drug pharmacodynamic efficacy sub-network and the reference drugpharmacodynamic efficacy sub-network assigning a second score to thepatient drug pharmacodynamic adverse event sub-network based on anamount of similarity between the patient drug pharmacodynamic adverseevent sub-network and the reference drug pharmacodynamic adverse-eventsub-network; and.
 11. The method of claim 10, wherein determining toadminister the NMDAR antagonist or partial antagonist, GLRB modulator,or AMPAR agonist for administering to the patient based on thecomparison includes: determining to administer the NMDAR antagonist orpartial antagonist, GLRB modulator, or AMPAR agonist for the patientwhen the first score is above a first threshold score or the secondscore is below a second threshold score.
 12. The method of claim 11,wherein determining to administer the NMDAR antagonist or partialantagonist, GLRB modulator, or AMPAR agonist for the patient when thefirst score is above a first threshold score of the second score isbelow a second threshold score includes: combining the first and secondscores to generate an overall score; and determining to administer theNMDAR antagonist or partial antagonist, GLRB modulator, or AMPAR agonistfor the patient when the overall score is above a third threshold score.13. The method of claim 9, further comprising: determining a dosage ofthe NMDAR antagonist or partial antagonist, GLRB modulator, or AMPARagonist for administering to the patient based on the comparison; andadministering the determined dosage of the NMDAR antagonist or partialantagonist, GLRB modulator, or AMPAR agonist to the patient.
 14. Themethod of claim 13, wherein determining a dosage of the NMDAR antagonistor partial antagonist, GLRB modulator, or AMPAR agonist foradministering to the patient includes: determining the dosage using aregression model based on a combination of two or more of: the sex ofthe patient, the age of the patient, whether the patient smokes,ethnicity of the patient, height of the patient, weight of the patient,and mental illness history of the patient.
 15. The method of claim 13,further comprising: obtaining clinical data for the patient; analyzingthe biological sample using pharmacometabolomics to determinepre-existing medications and metabolites of the pre-existing medicationsin the patient; and determining whether to administer the NMDARantagonist or partial antagonist, GLRB modulator, or AMPAR agonist tothe patient or the dosage to administer based at least in part ondrug-gene or drug-drug interactions between the pre-existing medicationsin the patient and the NMDAR antagonist or partial antagonist, GLRBmodulator, or AMPAR agonist.
 16. The method of claim 9, wherein the drugpharmacogenomic network for the NMDAR antagonist or partial antagonist,GLRB modulator, or AMPAR agonist from the reference database is aketamine pharmacogenomic network and includes one or more of: Activityregulated cytoskeleton associated protein (ARC) gene, Achaete-Scutefamily bHLH transcription factor 1 (ASCL1) gene, Brain derivedneurotrophic factor (BDNF) gene, BDNF antisense RNA (BDNF-AS) gene,Calcium/calmodulin dependent protein kinase II alpha (CAMK2A) gene,Cyclin dependent kinase inhibitor 1A (CDKN1A) gene, cAMP responsiveelement modulator (CREM) gene, Cut like homeobox 2 (CUX2) gene, DCCnetrin 1 receptor (DCC) gene, Dopamine receptor D2 (DRD2) gene,Eukaryotic translation elongation factor 2 kinase (EEF2K) gene, FragileX mental retardation 1 (FMR1) gene, Ganglioside induced differentiationassociated protein 1 like 1 (GDAP1L1) gene, Glutamate metabotropicreceptor 5 (GRM5) gene, Homer scaffold protein 1 (HOMER1) gene,5-hydroxytryptamine receptor 1B (HTR1B) gene, 5-hydroxytryptaminereceptor 2A (HTR2A) gene, Kruppel like factor 6 (KLF6) gene, Lin-7homolog C, crumbs cell polarity complex component (LIN7C) long noncodingRNA, LOC105379109 long noncoding RNA, Myocyte enhancer factor 2D (MEF2D)gene, Myosin VI (MYO6) gene, Myelin transcription factor 1 like (MYT1L)gene, Neuronal differentiation 1 (NEUROD1) gene, Neuronaldifferentiation 2 (NEUROD2) gene, Nescient helix-loop-helix 2 (NHLH2)gene, Neuromedin B (NMB) gene, NMDA receptor synaptonuclear signalingand neuronal migration factor (NSMF) gene, Neurotrophic receptortyrosine kinase 2 (NTRK2) gene, Phosphotase and tensin homolog (PTEN)gene, Prostaglandin-endoperoxide synthase 2 (PTGS2) gene, Rac familysmall GTPase 1 (RAC1) gene, Ras protein specific guanine nucleotidereleasing factor 2 (RASGRF2) gene, Ras homolog family member A (RHOA)gene, Roundabout guidance receptor 2 (ROBO2) gene, RP11_360A181 longnoncoding RNA, Semaphorin 3A (SEMA3A) gene, SH3 and multiple ankyrinrepeat domains 1 (SHANK1) gene, SH3 and multiple ankyrin repeat domains2 (SHANK2) gene, SH3 and multiple ankyrin repeat domains 3 (SHANK3)gene, Solute carrier family 22 member 15 (SLC22A15) gene, Solute carrierfamily 6 member 2 (SLC6A2) gene, Slit guidance ligand 1 (SLIT1) gene,Slit guidance ligand 2 (SLIT2) gene, Synaptosome associated protein 25(SNAP25) gene, Synapsin I (SYN1) gene, Synapsin II (SYN2) gene, SynapsinIII (SYN3) gene, T-box, brain 1 (TBR1) gene, Transcription factor 4(TCF4) gene, Acetylcholinesterase (ACHE) gene, Activating transcriptionfactor 7 interacting protein (ATF7IP) gene, Activating transcriptionfactor 7 interacting protein 2 (ATF7IP2) gene, ATPase Na+/K+Transporting Subunit Alpha 1 (ATP1A1) gene, BLOC-1 related complex unit7 (BORCS7) gene, Bromodomain containing 4 (BRD4) gene, Calciumvoltage-gated channel subunit alpha1 C (CACNA1C) gene, Calciumvoltage-gated channel auxiliary subunit beta 1 (CACNB1) gene, Calciumvoltage-gated channel auxiliary subunit beta 2 (CACNB2) gene, Calciumvoltage-gated channel auxiliary subunit gamma 2 (CACNG2) gene,Cholinergic Receptor Muscarinic 2 (CHRM2) gene, Cholinergic ReceptorNicotinic Alpha 3 Subunit (CHRNA3) gene, Cholinergic Receptor NicotinicAlpha 5 Subunit (CHRNA5) gene, Cholinergic Receptor Nicotinic Alpha 7Subunit (CHRNA7) gene, Cannabinoid receptor 1 (CNR1) gene, Disks largehomolog 3 (DLG3) gene, Disks large homolog 4 (DLG4) gene, DNAMethyltransferase 1 (DNMT1) gene, Euchromatic histone lysinemethyltransferase 1 (EHMT1) gene, Gamma-aminobutyric acid type Areceptor alpha2 subunit (GABRA2) gene, Gamma-aminobutyric acid type Areceptor alpha5 subunit (GABRA5) gene, Glutamate decarboxylase 1 (GAD1)gene, Glycine receptor alpha 1 (GLRA1) gene, Glycine receptor alpha 2(GLRA2) gene, Glycine receptor beta (GLRB) gene, Glutamate ionotropicreceptor AMPA type subunit 1 (GRIA1) gene, Glutamate ionotropic receptorAMPA type subunit 2 (GRIA2) gene, Glutamate ionotropic receptor AMPAtype subunit 2 (GRIA4) gene, Glutamate ionotropic receptor NMDA typesubunit 1 (GRIN1) gene, Glutamate ionotropic receptor NMDA type subunit2A (GRIN2A) gene, Glutamate ionotropic receptor NMDA type subunit 2B(GRIN2B) gene, Glutamate ionotropic receptor NMDA type subunit 2C(GRIN2C) gene, Glutamate ionotropic receptor NMDA type subunit 2D(GRIN2D) gene, Glutamate ionotropic receptor NMDA type subunit 3A(GRIN3A) gene, Glutamate ionotropic receptor NMDA type subunit 3B(GRIN3B) gene, Hyperpolarization Activated Cyclic Nucleotide GatedPotassium Channel 1 (HCN1) gene, Histone deacetylase 5 (HDAC5) gene,Methyl-CpG binding domain protein 1 (MBD1) gene, M-Phase Phosphoprotein8 (MPHOSPH8) gene, Neural cell adhesion molecule 1 (NCAM1) gene, Nitricacid synthase 1 (NOS1) gene, Nitric acid synthase 2 (NOS2) gene, Nitricacid synthase 3 (NOS3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1)gene, Opioid receptor kappa 1 (OPRK1) gene, Opioid receptor mu 1 (OPRM1)gene, Roundabout guidance receptor 2 (ROBO2) gene, SET domain bifurcated1 (SETDB1) gene, SH3 and Multiple Ankyrin Repeat Domains 2 (SHANK2)gene, Sigma Non-Opioid Intracellular Receptor 1 (SIGMAR1) gene, Solutecarrier family 6 member 9 (SLC6A9) gene, Transcription ActivationSuppressor (TASOR) gene, TOG array regulator of axonemal microtubules 2(TOGORAM2) gene, Tripartite Motif Containing 28 (TRIM28) gene, ZincFinger Protein 274 (ZNF274) gene, Anaphase promoting complex subunit 2(ANAPC2) gene, Cytochrome P450 family 2 subfamily A member 6 (CYP2A6)gene, Cytochrome P450 family 2 subfamily B member 6 (CYP2B6) gene,Cytochrome P450 family 3 subfamily A member 4 (CYP3A4) gene, Disks largehomolog 4 (DLG4), Eukaryotic Elongation Factor 2 Kinase (EEF2K) gene,Estrogen Receptor 1 (ESR1) gene, Glutamate ionotropic receptor AMPA typesubunit 1 (GRIA1) gene, Glutamate ionotropic receptor AMPA type subunit2 (GRIA4) gene, Glutamate ionotropic receptor NMDA type subunit 1(GRIN1) gene, Glutamate ionotropic receptor NMDA type subunit 2B(GRIN2B) gene, Myosin VI (MYO6) gene, Roundabout Guidance Receptor 2(ROBO2) gene, SH3 and Multiple Ankyrin Repeat Domains 2 (SHANK2) gene,or Transcription Elongation Regulator 1 (TCERG1) gene.
 17. The method ofclaim 16, wherein the constituent sub-networks for the ketaminepharmacogenomic network include a drug pharmacodynamic efficacysub-network, and wherein the drug pharmacodynamic efficacy sub-networkincludes one or more of: Activity regulated cytoskeleton associatedprotein (ARC) gene, Achaete-Scute family bHLH transcription factor 1(ASCL1) gene, Brain derived neurotrophic factor (BDNF) gene, BDNFantisense RNA (BDNF-AS) gene, Calcium/calmodulin dependent proteinkinase II alpha (CAMK2A) gene, Cyclin dependent kinase inhibitor 1A(CDKN1A) gene, cAMP responsive element modulator (CREM) gene, Cut likehomeobox 2 (CUX2) gene, DCC netrin 1 receptor (DCC) gene, Dopaminereceptor D2 (DRD2) gene, Eukaryotic translation elongation factor 2kinase (EEF2K) gene, Fragile X mental retardation 1 (FMR1) gene,Ganglioside induced differentiation associated protein 1 like 1(GDAP1L1) gene, Glutamate metabotropic receptor 5 (GRM5) gene, Homerscaffold protein 1 (HOMER1) gene, 5-hydroxytryptamine receptor 1B(HTR1B) gene, 5-hydroxytryptamine receptor 2A (HTR2A) gene, Kruppel likefactor 6 (KLF6) gene, Lin-7 homolog C, crumbs cell polarity complexcomponent (LIN7C) long noncoding RNA, LOC105379109 long noncoding RNA,Myocyte enhancer factor 2D (MEF2D) gene, Myosin VI (MYO6) gene, Myelintranscription factor 1 like (MYT1L) gene, Neuronal differentiation 1(NEUROD1) gene, Neuronal differentiation 2 (NEUROD2) gene, Nescienthelix-loop-helix 2 (NHLH2) gene, Neuromedin B (NMB) gene, NMDA receptorsynaptonuclear signaling and neuronal migration factor (NSMF) gene,Neurotrophic receptor tyrosine kinase 2 (NTRK2) gene, Phosphotase andtensin homolog (PTEN) gene, Prostaglandin-endoperoxide synthase 2(PTGS2) gene, Rac family small GTPase 1 (RAC1) gene, Ras proteinspecific guanine nucleotide releasing factor 2 (RASGRF2) gene, Rashomolog family member A (RHOA) gene, Roundabout guidance receptor 2(ROBO2) gene, RP11_360A181 long noncoding RNA, Semaphorin 3A (SEMA3A)gene, SH3 and multiple ankyrin repeat domains 1 (SHANK1) gene, SH3 andmultiple ankyrin repeat domains 2 (SHANK2) gene, SH3 and multipleankyrin repeat domains 3 (SHANK3) gene, Solute carrier family 22 member15 (SLC22A15) gene, Solute carrier family 6 member 2 (SLC6A2) gene, Slitguidance ligand 1 (SLIT1) gene, Slit guidance ligand 2 (SLIT2) gene,Synaptosome associated protein 25 (SNAP25) gene, Synapsin I (SYN1) gene,Synapsin II (SYN2) gene, Synapsin III (SYN3) gene, T-box, brain 1 (TBR1)gene, or Transcription factor 4 (TCF4) gene.
 18. The method of claim 16,wherein the constituent sub-networks for the ketamine pharmacogenomicnetwork include a drug pharmacodynamic adverse events sub-network, andwherein the drug pharmacodynamic adverse events sub-network includes oneor more of: Acetylcholinesterase (ACHE) gene, Activating transcriptionfactor 7 interacting protein (ATF7IP) gene, Activating transcriptionfactor 7 interacting protein 2 (ATF7IP2) gene, ATPase Na+/K+Transporting Subunit Alpha 1 (ATP1A1) gene, BLOC-1 related complex unit7 (BORCS7) gene, Bromodomain containing 4 (BRD4) gene, Calciumvoltage-gated channel subunit alpha1 C (CACNA1C) gene, Calciumvoltage-gated channel auxiliary subunit beta 1 (CACNB1) gene, Calciumvoltage-gated channel auxiliary subunit beta 2 (CACNB2) gene, Calciumvoltage-gated channel auxiliary subunit gamma 2 (CACNG2) gene,Cholinergic Receptor Muscarinic 2 (CHRM2) gene, Cholinergic ReceptorNicotinic Alpha 3 Subunit (CHRNA3) gene, Cholinergic Receptor NicotinicAlpha 5 Subunit (CHRNA5) gene, Cholinergic Receptor Nicotinic Alpha 7Subunit (CHRNA7) gene, Cannabinoid receptor 1 (CNR1) gene, Disks largehomolog 3 (DLG3) gene, Disks large homolog 4 (DLG4) gene, DNAMethyltransferase 1 (DNMT1) gene, Euchromatic histone lysinemethyltransferase 1 (EHMT1) gene, Gamma-aminobutyric acid type Areceptor alpha2 subunit (GABRA2) gene, Gamma-aminobutyric acid type Areceptor alpha5 subunit (GABRA5) gene, Glutamate decarboxylase 1 (GAD1)gene, Glycine receptor alpha 1 (GLRA1) gene, Glycine receptor alpha 2(GLRA2) gene, Glycine receptor beta (GLRB) gene, Glutamate ionotropicreceptor AMPA type subunit 1 (GRIA1) gene, Glutamate ionotropic receptorAMPA type subunit 2 (GRIA2) gene, Glutamate ionotropic receptor AMPAtype subunit 2 (GRIA4) gene, Glutamate ionotropic receptor NMDA typesubunit 1 (GRIN1) gene, Glutamate ionotropic receptor NMDA type subunit2A (GRIN2A) gene, Glutamate ionotropic receptor NMDA type subunit 2B(GRIN2B) gene, Glutamate ionotropic receptor NMDA type subunit 2C(GRIN2C) gene, Glutamate ionotropic receptor NMDA type subunit 2D(GRIN2D) gene, Glutamate ionotropic receptor NMDA type subunit 3A(GRIN3A) gene, Glutamate ionotropic receptor NMDA type subunit 3B(GRIN3B) gene, Hyperpolarization Activated Cyclic Nucleotide GatedPotassium Channel 1 (HCN1) gene, Histone deacetylase 5 (HDAC5) gene,Methyl-CpG binding domain protein 1 (MBD1) gene, M-Phase Phosphoprotein8 (MPHOSPH8) gene, Neural cell adhesion molecule 1 (NCAM1) gene, Nitricacid synthase 1 (NOS1) gene, Nitric acid synthase 2 (NOS2) gene, Nitricacid synthase 3 (NOS3) gene, NAD(P)H quinone dehydrogenase 1 (NQO1)gene, Opioid receptor kappa 1 (OPRK1) gene, Opioid receptor mu 1 (OPRM1)gene, Roundabout guidance receptor 2 (ROBO2) gene, SET domain bifurcated1 (SETDB1) gene, SH3 and Multiple Ankyrin Repeat Domains 2 (SHANK2)gene, Sigma Non-Opioid Intracellular Receptor 1 (SIGMAR1) gene, Solutecarrier family 6 member 9 (SLC6A9) gene, Transcription ActivationSuppressor (TASOR) gene, TOG array regulator of axonemal microtubules 2(TOGORAM2) gene, Tripartite Motif Containing 28 (TRIM28) gene, or ZincFinger Protein 274 (ZNF274) gene.
 19. The method of claim 16, whereinthe constituent sub-networks for the ketamine pharmacogenomic networkinclude a pharmacokinetic enzymes and hormones sub-network, and whereinthe pharmacokinetic enzymes and hormones sub-network includes one ormore of: Anaphase promoting complex subunit 2 (ANAPC2) gene, CytochromeP450 family 2 subfamily A member 6 (CYP2A6) gene, Cytochrome P450 family2 subfamily B member 6 (CYP2B6) gene, Cytochrome P450 family 3 subfamilyA member 4 (CYP3A4) gene, Disks large homolog 4 (DLG4), EukaryoticElongation Factor 2 Kinase (EEF2K) gene, Estrogen Receptor 1 (ESR1)gene, Glutamate ionotropic receptor AMPA type subunit 1 (GRIA1) gene,Glutamate ionotropic receptor AMPA type subunit 2 (GRIA4) gene,Glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) gene,Glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B) gene, MyosinVI (MYO6) gene, Roundabout Guidance Receptor 2 (ROBO2) gene, SH3 andMultiple Ankyrin Repeat Domains 2 (SHANK2) gene, or TranscriptionElongation Regulator 1 (TCERG1) gene.